Location: Annapolis Junction, MD, United States
Job Title
Senior‑Level Systems Engineer
Why IDS?
IDS believes in resolving conflict, building innovative approaches to do so. Combining operational expertise with an intimate understanding of today’s greatest challenges, we bring our customers the solutions required for success in a complex and multidimensional world. IDS tailors solutions for a diverse range of government, military, nonprofit, and public‑sector clients.
Locations
Annapolis Junction, MD
Shifts Available
Please note, candidates must be flexible. Shift times are general hours; some projects may require earlier or later reporting based on customer requirements.
Job Description
IDS is seeking a Senior‑Level Systems Engineer to support the definition and implementation of system architecture for an Agent Knowledge Management System utilizing an agentic framework for automated knowledge extraction. The engineer will work closely with project leadership to manage system integration, deployment strategy, compliance, testing, validation, and lifecycle sustainment.
Key Duties
• Collaborate with project leadership to manage system integration efforts across multiple components and teams
• Develop, maintain, and execute deployment strategies and processes to ensure smooth product delivery
• Coordinate technical efforts across engineering, data science, and UX teams to support integrated delivery
• Ensure compliance with system standards and best practices through testing, validation, and lifecycle sustainment
• Decompose project requirements into actionable technical tasks and specifications
• Develop and maintain technical plans, milestones, and support code review activities
• Maintain system architecture, integration, and deployment documentation
• Conduct security, performance, and quality audits to ensure compliance
Requirements
• Bachelor’s degree with nine (9) years of relevant experience, or Master’s degree with seven (7) years, or Doctoral degree with five (5) years
• Degree in Engineering, Physics, Astronomy, or a related technical field (e.g., Computer Science, Mathematics)
• Experience designing and sustaining engineering systems across the full system lifecycle
• Experience decomposing requirements and supporting system integration efforts
• Familiarity with Agile development and CI/CD practices
• Active TS/SCI with Polygraph
Compensation
• Salary range: $150,000 – $170,000
• Benefits
Location: Multiple Countries
About Improvado
Improvado is an AI-powered marketing analytics platform. It connects data from 1000+ marketing and sales sources, cleans and organizes it, and lets teams analyze it and act on it through an AI Agent, without manual reporting. Our customers are marketers, agencies and analytics teams at mid-size and enterprise companies.
The Role
We're looking for a UGC Creator to make short, authentic videos about Improvado and its products. You'll explain what we do and show how it helps marketers, in a real, human voice rather than a polished ad voice.
What you'll do
Create short videos on the product use cases, walkthroughs, "how I'd use this" stories and explainers of our features and AI Agent
Turn briefs into your own script and storytelling (we give you the product input, you make it engaging)
Handle filming, voiceover, editing, captions and on-screen text
What we're looking for
3+ UGC or video projects in your portfolio (required)
Confident on camera, with a natural, conversational delivery
Strong English
Able to explain a technical or B2B product in simple words
Solid editing skills (CapCut, Premiere, Descript or similar)
Reliable with weekly deadlines
Nice to have: experience with B2B SaaS, marketing or analytics tools, or AI products
Details
Format: freelance, remote
Commitment: 1 × ~3-min video per week
Time zone: must be available for syncs in PST
Please share the link to your portfolio with at least 3 available UGC videos.
Location: (Multiple States), United States
About Improvado
Improvado is a marketing data and AI analytics platform for enterprise brands and agencies. We connect 500+ marketing and sales data sources, normalize them through a governed data model, and put an AI agent on top so marketing teams can ask questions of their own data without going through BI or IT.
What we sell:
extraction and 500+ connectors; data transformation (SQL and dbt-compatible); Marketing Data Governance — taxonomy, naming, data-quality rules; warehouse and storage; dashboards; AI agent and MCP access to live marketing data; DECS, data engineering as a service on a 2 to 4 week SLA.
Who buys it:
Economic buyers:
CMO, VP Marketing, VP Demand Gen, agency COO and ops leadership
Technical evaluators:
VP or Director of Marketing Analytics, marketing analysts, data engineering, sometimes IT and security
Segments:
enterprise brands, mid-market, agencies
Verticals:
pharma and healthcare, financial services, retail and eCommerce, gaming, agencies
Why this role, now
Our messaging is not aligned.
Positioning, web, sales pitch, product and content each carry a different version of what Improvado is. The market categorizes us as "marketing ETL" while we position as an AI marketing analytics platform. That gap costs us deal quality and price.
The category is crowded and moving fast.
Battlecards exist but decay, and nobody owns keeping them true.
Attribution, incrementality and causal measurement is where deals are won and lost
— and it is the hardest thing to argue credibly without hand-waving.
Analyst firms are a silent blocker.
When an enterprise buyer asks Gartner or Forrester about Improvado, there is no briefed narrative waiting for them.
You would not start from a blank page. We already have a positioning registry, roughly 11 documented buyer personas, a pain-point and value-prop library, competitor intel reports and about 10 battlecards. They are inconsistent and under-used. Your first job is to make one version true and make people use it.
What you'll own
Positioning and messaging.
Core positioning, category framing and message hierarchy across ICPs and personas. ICP and persona documentation that is used, not filed. The messaging layer of new product and feature launches, including the internal rollout that makes the field able to sell it.
Competitive intelligence and win/loss.
A structured win/loss interview program with quarterly readouts. Living battlecards. Monitoring competitor positioning, pricing and product moves, and driving our counter-positioning.
Sales enablement.
The core pitch narrative, demo storyline and objection-handling framework. The messaging components of new-rep onboarding. A standing weekly cadence with Sales leadership — not quarterly check-ins. Direct support on competitive and enterprise deals: RFPs, security and technical evaluations, displacement plays.
Market and analyst relations.
The Improvado narrative for Gartner, Forrester and category influencers, so that a buyer's analyst call becomes an accelerator rather than a blocker. Feeding structured market and buyer insight back into product prioritization.
What we're looking for
4 to 8 years in product marketing, or an equivalent blend of product marketing with marketing analytics, marketing or data ops, sales engineering, analytics consulting, or product management on a data product.
Category fluency in marketing data and measurement: attribution models, incrementality testing, MMM, data governance and taxonomy, warehouses and ETL/ELT — including where each is honestly weak.
B2B SaaS sold to marketing or data buyers. Marketing analytics, martech, measurement, CDP, BI and data-infrastructure vendors are the reference profile.
Proof you have shipped, not just documented: positioning that changed the website and the pitch, battlecards reps actually opened, a win/loss program that survived past its second quarter.
Writing that survives contact with a skeptical technical buyer — claim and evidence, not adjective stacking.
Comfort operating close to Sales: weekly cadence, deal reviews, live calls, direct feedback.
Nice to have:
hands-on SQL, dbt or BI, enough to validate a claim yourself. Experience marketing AI capabilities without hype. Analyst-relations experience. Agency-side or enterprise-brand background. Vertical depth in pharma and healthcare, financial services, retail and eCommerce, or gaming.
Not a fit if:
your product marketing has been mostly launch logistics and content calendars; you need a fully staffed team and a finished brand system to be effective; you are uncomfortable being told in a deal review that your messaging did not work.
How we measure success
Win rate in competitive deals against our top 3 named competitors, baseline set in month 1.
Stage-to-stage conversion on open opportunities, particularly discovery to demo to proposal.
Time-to-first-deal for new reps.
Field adoption of enablement assets — measured, not assumed.
Win/loss program running on cadence, with themes that visibly change product and GTM decisions.
Message consistency: a quarterly audit across web, deck, product UI and content.
What We Offer
Remote-first environment
Competitive compensation
Medical and dental benefits
401K plan
Unlimited PTO
Paid holidays
Professional development reimbursement
Location: (Multiple States), United States
About Improvado
Improvado is a marketing data and AI analytics platform for enterprise brands and agencies. We connect 500+ marketing and sales data sources, normalize them through a governed data model, and put an AI agent on top so marketing teams can ask questions of their own data without going through BI or IT.
What we sell:
extraction and 500+ connectors; data transformation (SQL and dbt-compatible); Marketing Data Governance — taxonomy, naming, data-quality rules; warehouse and storage; dashboards; AI agent and MCP access to live marketing data; DECS, data engineering as a service on a 2 to 4 week SLA.
Who buys it:
Economic buyers:
CMO, VP Marketing, VP Demand Gen, agency COO and ops leadership
Technical evaluators:
VP or Director of Marketing Analytics, marketing analysts, data engineering, sometimes IT and security
Segments:
enterprise brands, mid-market, agencies
Verticals:
pharma and healthcare, financial services, retail and eCommerce, gaming, agencies
Why this role, now
Our messaging is not aligned.
Positioning, web, sales pitch, product and content each carry a different version of what Improvado is. The market categorizes us as "marketing ETL" while we position as an AI marketing analytics platform. That gap costs us deal quality and price.
The category is crowded and moving fast.
Battlecards exist but decay, and nobody owns keeping them true.
Attribution, incrementality and causal measurement is where deals are won and lost
— and it is the hardest thing to argue credibly without hand-waving.
Analyst firms are a silent blocker.
When an enterprise buyer asks Gartner or Forrester about Improvado, there is no briefed narrative waiting for them.
You would not start from a blank page. We already have a positioning registry, roughly 11 documented buyer personas, a pain-point and value-prop library, competitor intel reports and about 10 battlecards. They are inconsistent and under-used. Your first job is to make one version true and make people use it.
What you'll own
Positioning and messaging.
Core positioning, category framing and message hierarchy across ICPs and personas. ICP and persona documentation that is used, not filed. The messaging layer of new product and feature launches, including the internal rollout that makes the field able to sell it.
Competitive intelligence and win/loss.
A structured win/loss interview program with quarterly readouts. Living battlecards. Monitoring competitor positioning, pricing and product moves, and driving our counter-positioning.
Sales enablement.
The core pitch narrative, demo storyline and objection-handling framework. The messaging components of new-rep onboarding. A standing weekly cadence with Sales leadership — not quarterly check-ins. Direct support on competitive and enterprise deals: RFPs, security and technical evaluations, displacement plays.
Market and analyst relations.
The Improvado narrative for Gartner, Forrester and category influencers, so that a buyer's analyst call becomes an accelerator rather than a blocker. Feeding structured market and buyer insight back into product prioritization.
What we're looking for
4 to 8 years in product marketing, or an equivalent blend of product marketing with marketing analytics, marketing or data ops, sales engineering, analytics consulting, or product management on a data product.
Category fluency in marketing data and measurement: attribution models, incrementality testing, MMM, data governance and taxonomy, warehouses and ETL/ELT — including where each is honestly weak.
B2B SaaS sold to marketing or data buyers. Marketing analytics, martech, measurement, CDP, BI and data-infrastructure vendors are the reference profile.
Proof you have shipped, not just documented: positioning that changed the website and the pitch, battlecards reps actually opened, a win/loss program that survived past its second quarter.
Writing that survives contact with a skeptical technical buyer — claim and evidence, not adjective stacking.
Comfort operating close to Sales: weekly cadence, deal reviews, live calls, direct feedback.
Nice to have:
hands-on SQL, dbt or BI, enough to validate a claim yourself. Experience marketing AI capabilities without hype. Analyst-relations experience. Agency-side or enterprise-brand background. Vertical depth in pharma and healthcare, financial services, retail and eCommerce, or gaming.
Not a fit if:
your product marketing has been mostly launch logistics and content calendars; you need a fully staffed team and a finished brand system to be effective; you are uncomfortable being told in a deal review that your messaging did not work.
How we measure success
Win rate in competitive deals against our top 3 named competitors, baseline set in month 1.
Stage-to-stage conversion on open opportunities, particularly discovery to demo to proposal.
Time-to-first-deal for new reps.
Field adoption of enablement assets — measured, not assumed.
Win/loss program running on cadence, with themes that visibly change product and GTM decisions.
Message consistency: a quarterly audit across web, deck, product UI and content.
What We Offer
Remote-first environment
Competitive compensation
Medical and dental benefits
401K plan
Unlimited PTO
Paid holidays
Professional development reimbursement
Location: Serbia
We're a fast-growing Series A startup ($34M raised, $10.3M ARR, 75% gross margin) building the unified marketing intelligence platform for enterprises like Spotify, Asus, and Colgate. Our Platform Team owns the core data pipeline infrastructure powering 500+ integrations — and we're now rebuilding how that work gets done, with AI at the center.
We're looking for a Principal Engineer who can set the technical direction for AI-assisted engineering across the Platform Team — someone who doesn't just use AI tools, but defines how they're used responsibly at scale.
About the Role
You'll be the architectural voice on the Platform Team, shaping how we design, build, and evolve our core Python-based extraction and load infrastructure. More than that, you'll own the engineering practices shift: defining how agentic coding, AI-assisted development, and LLM tooling integrate into our delivery process without sacrificing reliability or maintainability. This is a hands-on role — you'll be writing code, reviewing architecture, and mentoring engineers, not just advising.
Responsibilities
Own platform architecture: design and evolve data extraction, load, and pipeline systems serving 500+ integrations at scale
Identify architectural bottlenecks and drive performance/reliability improvements at scale (Kubernetes, distributed systems, ClickHouse/PostgreSQL)
Lead modernization of legacy systems into scalable, AI-native infrastructure
Design and implement agentic development frameworks for codebase maintenance and continuous improvement — owning the full loop from AI-assisted development to automated acceptance and rollout, with proper validation, testing, and architectural guardrails
Make and document key technical decisions; set the bar for code quality, system reliability, and performance
Mentor and level up engineers on AI-assisted development practices — help them ship faster without introducing hidden risk
Collaborate cross-functionally with product and AI teams on agentic framework rollout and new integration capabilities
Requirements
7+ years of backend engineering (hands-on programming), with at least 2 years in a principal/architect-level role owning production systems
You've designed and scaled complex systems, not just maintained them
Hands-on experience with AI-assisted development (Claude Code, agentic coding) — you ship with it, know its limits and where they break, and have built agentic frameworks for code maintenance and rollout
Strong distributed systems background: Kubernetes, workflow orchestration (e.g. Temporal), message queues (e.g. RabbitMQ)
Experience building secure systems that are hard to break, hard to breach, and resilient
Experience with reliability practices at scale: observability, alerting, incident response
Solid SQL and query optimization across PostgreSQL and ClickHouse
Comfortable with ambiguity and high-velocity, fast-shifting priorities — we move like a startup, not a committee
Ability to work within EST timezone
Fluency in English
Nice to Have
Track record of defining engineering standards and driving adoption across a team
Experience migrating legacy pipeline systems
Background in data integration or ETL infrastructure
What We Offer
Remote-first environment
Direct impact on platform architecture — your decisions matter
Modern AI-native tech stack with real freedom to experiment
20 PTO days + US holidays
Stock options
Professional development reimbursement
Optional relocation assistance to Latin America after trial period
Location: Georgia
We're a fast-growing Series A startup ($34M raised, $10.3M ARR, 75% gross margin) building the unified marketing intelligence platform for enterprises like Spotify, Asus, and Colgate. Our Platform Team owns the core data pipeline infrastructure powering 500+ integrations — and we're now rebuilding how that work gets done, with AI at the center.
We're looking for a Principal Engineer who can set the technical direction for AI-assisted engineering across the Platform Team — someone who doesn't just use AI tools, but defines how they're used responsibly at scale.
About the Role
You'll be the architectural voice on the Platform Team, shaping how we design, build, and evolve our core Python-based extraction and load infrastructure. More than that, you'll own the engineering practices shift: defining how agentic coding, AI-assisted development, and LLM tooling integrate into our delivery process without sacrificing reliability or maintainability. This is a hands-on role — you'll be writing code, reviewing architecture, and mentoring engineers, not just advising.
Responsibilities
Own platform architecture: design and evolve data extraction, load, and pipeline systems serving 500+ integrations at scale
Identify architectural bottlenecks and drive performance/reliability improvements at scale (Kubernetes, distributed systems, ClickHouse/PostgreSQL)
Lead modernization of legacy systems into scalable, AI-native infrastructure
Design and implement agentic development frameworks for codebase maintenance and continuous improvement — owning the full loop from AI-assisted development to automated acceptance and rollout, with proper validation, testing, and architectural guardrails
Make and document key technical decisions; set the bar for code quality, system reliability, and performance
Mentor and level up engineers on AI-assisted development practices — help them ship faster without introducing hidden risk
Collaborate cross-functionally with product and AI teams on agentic framework rollout and new integration capabilities
Requirements
7+ years of backend engineering (hands-on programming), with at least 2 years in a principal/architect-level role owning production systems
You've designed and scaled complex systems, not just maintained them
Hands-on experience with AI-assisted development (Claude Code, agentic coding) — you ship with it, know its limits and where they break, and have built agentic frameworks for code maintenance and rollout
Strong distributed systems background: Kubernetes, workflow orchestration (e.g. Temporal), message queues (e.g. RabbitMQ)
Experience building secure systems that are hard to break, hard to breach, and resilient
Experience with reliability practices at scale: observability, alerting, incident response
Solid SQL and query optimization across PostgreSQL and ClickHouse
Comfortable with ambiguity and high-velocity, fast-shifting priorities — we move like a startup, not a committee
Ability to work within EST timezone
Fluency in English
Nice to Have
Track record of defining engineering standards and driving adoption across a team
Experience migrating legacy pipeline systems
Background in data integration or ETL infrastructure
What We Offer
Remote-first environment
Direct impact on platform architecture — your decisions matter
Modern AI-native tech stack with real freedom to experiment
20 PTO days + US holidays
Stock options
Professional development reimbursement
Optional relocation assistance to Latin America after trial period
Location: Armenia
We're a fast-growing Series A startup ($34M raised, $10.3M ARR, 75% gross margin) building the unified marketing intelligence platform for enterprises like Spotify, Asus, and Colgate. Our Platform Team owns the core data pipeline infrastructure powering 500+ integrations — and we're now rebuilding how that work gets done, with AI at the center.
We're looking for a Principal Engineer who can set the technical direction for AI-assisted engineering across the Platform Team — someone who doesn't just use AI tools, but defines how they're used responsibly at scale.
About the Role
You'll be the architectural voice on the Platform Team, shaping how we design, build, and evolve our core Python-based extraction and load infrastructure. More than that, you'll own the engineering practices shift: defining how agentic coding, AI-assisted development, and LLM tooling integrate into our delivery process without sacrificing reliability or maintainability. This is a hands-on role — you'll be writing code, reviewing architecture, and mentoring engineers, not just advising.
Responsibilities
Own platform architecture: design and evolve data extraction, load, and pipeline systems serving 500+ integrations at scale
Identify architectural bottlenecks and drive performance/reliability improvements at scale (Kubernetes, distributed systems, ClickHouse/PostgreSQL)
Lead modernization of legacy systems into scalable, AI-native infrastructure
Design and implement agentic development frameworks for codebase maintenance and continuous improvement — owning the full loop from AI-assisted development to automated acceptance and rollout, with proper validation, testing, and architectural guardrails
Make and document key technical decisions; set the bar for code quality, system reliability, and performance
Mentor and level up engineers on AI-assisted development practices — help them ship faster without introducing hidden risk
Collaborate cross-functionally with product and AI teams on agentic framework rollout and new integration capabilities
Requirements
7+ years of backend engineering (hands-on programming), with at least 2 years in a principal/architect-level role owning production systems
You've designed and scaled complex systems, not just maintained them
Hands-on experience with AI-assisted development (Claude Code, agentic coding) — you ship with it, know its limits and where they break, and have built agentic frameworks for code maintenance and rollout
Strong distributed systems background: Kubernetes, workflow orchestration (e.g. Temporal), message queues (e.g. RabbitMQ)
Experience building secure systems that are hard to break, hard to breach, and resilient
Experience with reliability practices at scale: observability, alerting, incident response
Solid SQL and query optimization across PostgreSQL and ClickHouse
Comfortable with ambiguity and high-velocity, fast-shifting priorities — we move like a startup, not a committee
Ability to work within EST timezone
Fluency in English
Nice to Have
Track record of defining engineering standards and driving adoption across a team
Experience migrating legacy pipeline systems
Background in data integration or ETL infrastructure
What We Offer
Remote-first environment
Direct impact on platform architecture — your decisions matter
Modern AI-native tech stack with real freedom to experiment
20 PTO days + US holidays
Stock options
Professional development reimbursement
Optional relocation assistance to Latin America after trial period
Location: Brazil
We're a fast-growing Series A startup ($34M raised, $10.3M ARR, 75% gross margin) building the unified marketing intelligence platform for enterprises like Spotify, Asus, and Colgate. Our Platform Team owns the core data pipeline infrastructure powering 500+ integrations — and we're now rebuilding how that work gets done, with AI at the center.
We're looking for a Principal Engineer who can set the technical direction for AI-assisted engineering across the Platform Team — someone who doesn't just use AI tools, but defines how they're used responsibly at scale.
About the Role
You'll be the architectural voice on the Platform Team, shaping how we design, build, and evolve our core Python-based extraction and load infrastructure. More than that, you'll own the engineering practices shift: defining how agentic coding, AI-assisted development, and LLM tooling integrate into our delivery process without sacrificing reliability or maintainability. This is a hands-on role — you'll be writing code, reviewing architecture, and mentoring engineers, not just advising.
Responsibilities
Own platform architecture: design and evolve data extraction, load, and pipeline systems serving 500+ integrations at scale
Identify architectural bottlenecks and drive performance/reliability improvements at scale (Kubernetes, distributed systems, ClickHouse/PostgreSQL)
Lead modernization of legacy systems into scalable, AI-native infrastructure
Design and implement agentic development frameworks for codebase maintenance and continuous improvement — owning the full loop from AI-assisted development to automated acceptance and rollout, with proper validation, testing, and architectural guardrails
Make and document key technical decisions; set the bar for code quality, system reliability, and performance
Mentor and level up engineers on AI-assisted development practices — help them ship faster without introducing hidden risk
Collaborate cross-functionally with product and AI teams on agentic framework rollout and new integration capabilities
Requirements
7+ years of backend engineering (hands-on programming), with at least 2 years in a principal/architect-level role owning production systems
You've designed and scaled complex systems, not just maintained them
Hands-on experience with AI-assisted development (Claude Code, agentic coding) — you ship with it, know its limits and where they break, and have built agentic frameworks for code maintenance and rollout
Strong distributed systems background: Kubernetes, workflow orchestration (e.g. Temporal), message queues (e.g. RabbitMQ)
Experience building secure systems that are hard to break, hard to breach, and resilient
Experience with reliability practices at scale: observability, alerting, incident response
Solid SQL and query optimization across PostgreSQL and ClickHouse
Comfortable with ambiguity and high-velocity, fast-shifting priorities — we move like a startup, not a committee
Ability to work within EST timezone
Fluency in English
Nice to Have
Track record of defining engineering standards and driving adoption across a team
Experience migrating legacy pipeline systems
Background in data integration or ETL infrastructure
What We Offer
Remote-first environment
Direct impact on platform architecture — your decisions matter
Modern AI-native tech stack with real freedom to experiment
20 PTO days + US holidays
Stock options
Professional development reimbursement
Optional relocation assistance to Latin America after trial period
Location: Argentina
We're a fast-growing Series A startup ($34M raised, $10.3M ARR, 75% gross margin) building the unified marketing intelligence platform for enterprises like Spotify, Asus, and Colgate. Our Platform Team owns the core data pipeline infrastructure powering 500+ integrations — and we're now rebuilding how that work gets done, with AI at the center.
We're looking for a Principal Engineer who can set the technical direction for AI-assisted engineering across the Platform Team — someone who doesn't just use AI tools, but defines how they're used responsibly at scale.
About the Role
You'll be the architectural voice on the Platform Team, shaping how we design, build, and evolve our core Python-based extraction and load infrastructure. More than that, you'll own the engineering practices shift: defining how agentic coding, AI-assisted development, and LLM tooling integrate into our delivery process without sacrificing reliability or maintainability. This is a hands-on role — you'll be writing code, reviewing architecture, and mentoring engineers, not just advising.
Responsibilities
Own platform architecture: design and evolve data extraction, load, and pipeline systems serving 500+ integrations at scale
Identify architectural bottlenecks and drive performance/reliability improvements at scale (Kubernetes, distributed systems, ClickHouse/PostgreSQL)
Lead modernization of legacy systems into scalable, AI-native infrastructure
Design and implement agentic development frameworks for codebase maintenance and continuous improvement — owning the full loop from AI-assisted development to automated acceptance and rollout, with proper validation, testing, and architectural guardrails
Make and document key technical decisions; set the bar for code quality, system reliability, and performance
Mentor and level up engineers on AI-assisted development practices — help them ship faster without introducing hidden risk
Collaborate cross-functionally with product and AI teams on agentic framework rollout and new integration capabilities
Requirements
7+ years of backend engineering (hands-on programming), with at least 2 years in a principal/architect-level role owning production systems
You've designed and scaled complex systems, not just maintained them
Hands-on experience with AI-assisted development (Claude Code, agentic coding) — you ship with it, know its limits and where they break, and have built agentic frameworks for code maintenance and rollout
Strong distributed systems background: Kubernetes, workflow orchestration (e.g. Temporal), message queues (e.g. RabbitMQ)
Experience building secure systems that are hard to break, hard to breach, and resilient
Experience with reliability practices at scale: observability, alerting, incident response
Solid SQL and query optimization across PostgreSQL and ClickHouse
Comfortable with ambiguity and high-velocity, fast-shifting priorities — we move like a startup, not a committee
Ability to work within EST timezone
Fluency in English
Nice to Have
Track record of defining engineering standards and driving adoption across a team
Experience migrating legacy pipeline systems
Background in data integration or ETL infrastructure
What We Offer
Remote-first environment
Direct impact on platform architecture — your decisions matter
Modern AI-native tech stack with real freedom to experiment
20 PTO days + US holidays
Stock options
Professional development reimbursement
Optional relocation assistance to Latin America after trial period
Location: Serbia
AI Product Builder
Improvado
is an AI-powered, unified platform designed for marketing teams in medium to large-scale enterprises and agencies, who are looking to automate complex marketing intelligence and reporting to make decisions with ease. Improvado gathers, organizes, and untangles marketing data to deliver instant insights through BI and AI, helping to eliminate complexity, delivering laser-focused insights to optimize budgets, enhance campaign efficiency and marketing ROI.
About the role
As an AI Product Builder you own one product area end to end. Which area depends on where we need you most, and the work is the same in all of them. Our products turn marketing data into decisions, and each of them has the same failure mode: it tells the customer something and stops there. Your job is to find where your area stops short of causing a decision, build the part that closes that gap, and show that somebody acted. You will build hands-on with AI, shape the roadmap, work directly with engineering teams and with customers, and decide what a good recommendation looks like for a different persona on the same data - a media buyer, an analyst and a CMO do not want the same thing.
Responsibilities
Own the product area end to end, from idea to delivery, and define how the roadmap evolves
Close the gap between what the product reports and what the customer does about it: decide what gets recommended and to whom, and build the loop that makes those recommendations better over time
Work hands-on with AI, from mockups and prototypes through to production code for smaller features, alongside an engineering team of three to ten people
Run customer discovery yourself and turn it into roadmap decisions
Define and measure the metrics that prove the product caused someone to act
Build and maintain automated workflows using AI tools, scripting and the modern data stack
Collaborate with Product, BI and CSM teams on architecture decisions and customer data requirements
Qualifications
Product ownership experience
- able to take a problem end to end with minimal hand-holding, and point at things you built that are still in use
Demonstrated use of
AI tools
in daily work: Claude Code, agentic coding, prompt engineering, with a clear view of what you deliberately do not delegate
Experience
building an AI agent
and making it reliable in production
Marketing or revenue data domain
experience, and you have computed those metrics yourself rather than receiving them from an analyst
Comfortable working with engineers as a peer rather than across a handoff
Strong
SQL
;
Python
for data processing and automation
Fluent English
- there are customer calls and escalations in this role
Availability to overlap
US Pacific
business hours (or at least East)
Nice to have
Hands-on
ETL/ELT pipeline
experience (BigQuery, Snowflake, Redshift, or ClickHouse)
Experience working directly with clients on technical requirements
BI tools (e.g. Superset) for operational reporting
Process
A screening call, a 45-minute call with the VP of Product, a take-home designed for 90 minutes to two hours, and a review of it with our product managers.
What We Offer
Remote-first environment
Optional relocation assistance to Latin America after your trial period
Strong product/market fit: marketing data product for US-based enterprises
Modern AI tech stack and freedom to experiment
High-velocity team where you will constantly learn and scale your skills
High autonomy and ownership - we care about outcomes, not hours
AI-first, collaborative, no-bureaucracy culture with a technically strong, international team
20 working days of PTO per year
US holidays and additional days off
Professional development reimbursement
Location: Argentina
AI Product Builder
Improvado
is an AI-powered, unified platform designed for marketing teams in medium to large-scale enterprises and agencies, who are looking to automate complex marketing intelligence and reporting to make decisions with ease. Improvado gathers, organizes, and untangles marketing data to deliver instant insights through BI and AI, helping to eliminate complexity, delivering laser-focused insights to optimize budgets, enhance campaign efficiency and marketing ROI.
About the role
As an AI Product Builder you own one product area end to end. Which area depends on where we need you most, and the work is the same in all of them. Our products turn marketing data into decisions, and each of them has the same failure mode: it tells the customer something and stops there. Your job is to find where your area stops short of causing a decision, build the part that closes that gap, and show that somebody acted. You will build hands-on with AI, shape the roadmap, work directly with engineering teams and with customers, and decide what a good recommendation looks like for a different persona on the same data - a media buyer, an analyst and a CMO do not want the same thing.
Responsibilities
Own the product area end to end, from idea to delivery, and define how the roadmap evolves
Close the gap between what the product reports and what the customer does about it: decide what gets recommended and to whom, and build the loop that makes those recommendations better over time
Work hands-on with AI, from mockups and prototypes through to production code for smaller features, alongside an engineering team of three to ten people
Run customer discovery yourself and turn it into roadmap decisions
Define and measure the metrics that prove the product caused someone to act
Build and maintain automated workflows using AI tools, scripting and the modern data stack
Collaborate with Product, BI and CSM teams on architecture decisions and customer data requirements
Qualifications
Product ownership experience
- able to take a problem end to end with minimal hand-holding, and point at things you built that are still in use
Demonstrated use of
AI tools
in daily work: Claude Code, agentic coding, prompt engineering, with a clear view of what you deliberately do not delegate
Experience
building an AI agent
and making it reliable in production
Marketing or revenue data domain
experience, and you have computed those metrics yourself rather than receiving them from an analyst
Comfortable working with engineers as a peer rather than across a handoff
Strong
SQL
;
Python
for data processing and automation
Fluent English
- there are customer calls and escalations in this role
Availability to overlap
US Pacific
business hours (or at least East)
Nice to have
Hands-on
ETL/ELT pipeline
experience (BigQuery, Snowflake, Redshift, or ClickHouse)
Experience working directly with clients on technical requirements
BI tools (e.g. Superset) for operational reporting
Process
A screening call, a 45-minute call with the VP of Product, a take-home designed for 90 minutes to two hours, and a review of it with our product managers.
What We Offer
Remote-first environment
Optional relocation assistance to Latin America after your trial period
Strong product/market fit: marketing data product for US-based enterprises
Modern AI tech stack and freedom to experiment
High-velocity team where you will constantly learn and scale your skills
High autonomy and ownership - we care about outcomes, not hours
AI-first, collaborative, no-bureaucracy culture with a technically strong, international team
20 working days of PTO per year
US holidays and additional days off
Professional development reimbursement
Location: Georgia
AI Product Builder
Improvado
is an AI-powered, unified platform designed for marketing teams in medium to large-scale enterprises and agencies, who are looking to automate complex marketing intelligence and reporting to make decisions with ease. Improvado gathers, organizes, and untangles marketing data to deliver instant insights through BI and AI, helping to eliminate complexity, delivering laser-focused insights to optimize budgets, enhance campaign efficiency and marketing ROI.
About the role
As an AI Product Builder you own one product area end to end. Which area depends on where we need you most, and the work is the same in all of them. Our products turn marketing data into decisions, and each of them has the same failure mode: it tells the customer something and stops there. Your job is to find where your area stops short of causing a decision, build the part that closes that gap, and show that somebody acted. You will build hands-on with AI, shape the roadmap, work directly with engineering teams and with customers, and decide what a good recommendation looks like for a different persona on the same data - a media buyer, an analyst and a CMO do not want the same thing.
Responsibilities
Own the product area end to end, from idea to delivery, and define how the roadmap evolves
Close the gap between what the product reports and what the customer does about it: decide what gets recommended and to whom, and build the loop that makes those recommendations better over time
Work hands-on with AI, from mockups and prototypes through to production code for smaller features, alongside an engineering team of three to ten people
Run customer discovery yourself and turn it into roadmap decisions
Define and measure the metrics that prove the product caused someone to act
Build and maintain automated workflows using AI tools, scripting and the modern data stack
Collaborate with Product, BI and CSM teams on architecture decisions and customer data requirements
Qualifications
Product ownership experience
- able to take a problem end to end with minimal hand-holding, and point at things you built that are still in use
Demonstrated use of
AI tools
in daily work: Claude Code, agentic coding, prompt engineering, with a clear view of what you deliberately do not delegate
Experience
building an AI agent
and making it reliable in production
Marketing or revenue data domain
experience, and you have computed those metrics yourself rather than receiving them from an analyst
Comfortable working with engineers as a peer rather than across a handoff
Strong
SQL
;
Python
for data processing and automation
Fluent English
- there are customer calls and escalations in this role
Availability to overlap
US Pacific
business hours (or at least East)
Nice to have
Hands-on
ETL/ELT pipeline
experience (BigQuery, Snowflake, Redshift, or ClickHouse)
Experience working directly with clients on technical requirements
BI tools (e.g. Superset) for operational reporting
Process
A screening call, a 45-minute call with the VP of Product, a take-home designed for 90 minutes to two hours, and a review of it with our product managers.
What We Offer
Remote-first environment
Optional relocation assistance to Latin America after your trial period
Strong product/market fit: marketing data product for US-based enterprises
Modern AI tech stack and freedom to experiment
High-velocity team where you will constantly learn and scale your skills
High autonomy and ownership - we care about outcomes, not hours
AI-first, collaborative, no-bureaucracy culture with a technically strong, international team
20 working days of PTO per year
US holidays and additional days off
Professional development reimbursement
Location: Brazil
AI Product Builder
Improvado
is an AI-powered, unified platform designed for marketing teams in medium to large-scale enterprises and agencies, who are looking to automate complex marketing intelligence and reporting to make decisions with ease. Improvado gathers, organizes, and untangles marketing data to deliver instant insights through BI and AI, helping to eliminate complexity, delivering laser-focused insights to optimize budgets, enhance campaign efficiency and marketing ROI.
About the role
As an AI Product Builder you own one product area end to end. Which area depends on where we need you most, and the work is the same in all of them. Our products turn marketing data into decisions, and each of them has the same failure mode: it tells the customer something and stops there. Your job is to find where your area stops short of causing a decision, build the part that closes that gap, and show that somebody acted. You will build hands-on with AI, shape the roadmap, work directly with engineering teams and with customers, and decide what a good recommendation looks like for a different persona on the same data - a media buyer, an analyst and a CMO do not want the same thing.
Responsibilities
Own the product area end to end, from idea to delivery, and define how the roadmap evolves
Close the gap between what the product reports and what the customer does about it: decide what gets recommended and to whom, and build the loop that makes those recommendations better over time
Work hands-on with AI, from mockups and prototypes through to production code for smaller features, alongside an engineering team of three to ten people
Run customer discovery yourself and turn it into roadmap decisions
Define and measure the metrics that prove the product caused someone to act
Build and maintain automated workflows using AI tools, scripting and the modern data stack
Collaborate with Product, BI and CSM teams on architecture decisions and customer data requirements
Qualifications
Product ownership experience
- able to take a problem end to end with minimal hand-holding, and point at things you built that are still in use
Demonstrated use of
AI tools
in daily work: Claude Code, agentic coding, prompt engineering, with a clear view of what you deliberately do not delegate
Experience
building an AI agent
and making it reliable in production
Marketing or revenue data domain
experience, and you have computed those metrics yourself rather than receiving them from an analyst
Comfortable working with engineers as a peer rather than across a handoff
Strong
SQL
;
Python
for data processing and automation
Fluent English
- there are customer calls and escalations in this role
Availability to overlap
US Pacific
business hours (or at least East)
Nice to have
Hands-on
ETL/ELT pipeline
experience (BigQuery, Snowflake, Redshift, or ClickHouse)
Experience working directly with clients on technical requirements
BI tools (e.g. Superset) for operational reporting
Process
A screening call, a 45-minute call with the VP of Product, a take-home designed for 90 minutes to two hours, and a review of it with our product managers.
What We Offer
Remote-first environment
Optional relocation assistance to Latin America after your trial period
Strong product/market fit: marketing data product for US-based enterprises
Modern AI tech stack and freedom to experiment
High-velocity team where you will constantly learn and scale your skills
High autonomy and ownership - we care about outcomes, not hours
AI-first, collaborative, no-bureaucracy culture with a technically strong, international team
20 working days of PTO per year
US holidays and additional days off
Professional development reimbursement
Location: (Multiple States), United States
About Improvado
Improvado is an AI-powered marketing data platform built for mid-market and enterprise teams. We help companies automate complex marketing data workflows, unify data at scale, and surface insights through BI and AI. Brands like Genentech, T-Mobile, Activision, and H&R Block rely on us to simplify analytics and improve marketing performance.
We’re a fast-growing Series A startup backed by $34M in funding and driven by a team that moves fast, stays curious, and cares about high-quality execution.
About the Role
Improvado is hiring a Go-to-Market Engineer to build and run the engine that generates sales pipeline, then step into deals to help close them. You report directly to the VP of Sales.
You’ll build agentic go-to-market loops with the Improvado platform at their core, first for our own sales motion, then for our customers. Our GTM is the lab for what we sell, and you run it.
This is a team-of-one role with no SDR layer behind you. That means real ownership: you own outbound cadences, the data and tooling stack, and event follow-up. You work directly with the VP of Sales to design and run go-to-market experiments, on Improvado wherever possible, and you work alongside the demand generation team so outbound and advertising hit the same accounts and people. You identify which current customers to expand and with which products. And you are forward deployed: embedded with prospects and customers, building working use cases in their environment, and packaging the experiments that worked in-house into outcome-based projects Improvado sells.
It is a hybrid of growth operator, forward deployed builder, and revenue strategist. Most people come to this role strong in one or two of those areas, and we expect you to grow into the rest. If you have done two and are hungry for the third, keep reading.
Why you might love this job
Direct line to the VP of Sales: you design experiments together and see the results fast.
Your work moves pipeline every week, and nothing disappears into a process.
You build real, working things on a real platform, for us and for customers.
A clear path: become an Improvado expert who builds agentic GTM systems and delivers them to customers.
What You’ll Do
Outbound pipeline generation
Design, build, and run multi-channel outbound cadences (email, LinkedIn, phone) targeting enterprise marketing and data leaders and agency executives (agencies manage data for many brands at once, so one agency win can open several accounts)
Write and continuously test messaging, subject lines, and personalization at scale, including AI-assisted personalization
Own deliverability, sending infrastructure, and domain health
Sales and marketing alignment
Work with the demand generation team so outbound and advertising target the same accounts, personas, and buying committees, with one shared target account list
Build the operating rhythm between sales and marketing: shared definitions, a common view of account engagement, and a feedback loop from deals back into campaigns
Partner with marketing on content and offers; own the sourced-pipeline number on the outbound side
Go-to-market experimentation with the VP of Sales
Design and run go-to-market experiments with the VP of Sales: messaging by buyer persona, channels, offers, signals, and workflows, each with a hypothesis, a measurement, and a scale-or-kill decision
Run them on Improvado wherever possible, so our own go-to-market is the lab for what we sell
Maintain a messaging playbook per persona that outbound, advertising, and account executives all draw from, and share results with marketing on a regular cadence
Customer expansion
Analyze the customer base to identify which accounts we should be upselling and which Improvado products fit each
Build expansion plays with Customer Success and account executives, and run the outbound layer of those plays
Tooling and data
Own the go-to-market tech stack: sequencing, enrichment, intent data, CRM hygiene, and the integrations between them
Build and maintain target account lists and contact data with enrichment waterfalls and signal-based triggers
Automate the repetitive parts of prospecting so one person operates like a team
Event follow-up
Run pre-event targeting and outreach for conferences and field events
Convert attendees, booth visitors, and scanned leads into qualified meetings within days, not weeks
Forward deployed with prospects and customers
Learn the Improvado platform deeply: connectors, data models, the AI agent, and dashboards
Embed with prospects and customers on active opportunities and expansions to build working go-to-market and marketing-data use cases in their environment. Ship something they use, not a demo
Carry what you learn in customer environments back into our own experiments, outbound targeting, and messaging
Package and sell what works
Turn successful in-house experiments into packaged, outcome-based projects that Improvado sells to customers: the outcome, the data required, the build, and the delivery path
Help account executives position and scope those projects as part of the Improvado offer, and deliver the first ones yourself
Measurement
Track pipeline sourced and influenced by channel, cost per qualified meeting, and conversion through each stage
Share a short daily update with the VP of Sales (a quick async summary of numbers and learnings, not a long report) and stop what isn’t working quickly
Who You Are
4 or more years in a pipeline-generating or growth role at a B2B SaaS company: outbound, demand generation, growth, revenue operations, or forward deployed / implementation work, ideally more than one of these
You have personally built outbound cadences that booked meetings with enterprise buyers, and you can show the numbers
You have designed and run structured go-to-market experiments with a clear hypothesis and measurement, and you can tell the story of one that worked and one you killed
You have taken something that worked internally and turned it into something a customer paid for
You have worked across the sales and marketing boundary and know what it takes to get both teams targeting the same accounts
Technical enough to work with APIs, webhooks, SQL, and data pipelines, and to build on a data platform inside a customer’s environment
Fluent with AI tools for research, personalization, and automation, and interested in building agents, not just using them
Comfortable being a team of one: you set your own priorities, build your own systems, and do not wait for an SDR or an ops team
Strong writer. Your emails get replies
Curious about marketing data and analytics: you want to understand how a CMO measures performance, not just who to email (we’ll teach you the platform; the curiosity has to be yours)
Nice to have
Experience selling or marketing to agencies or enterprise marketing teams
Background in marketing analytics, ETL, data warehousing, or business intelligence
Forward-deployed, implementation, or professional services experience at a data or AI company
Experience with customer expansion or account-based programs
Hands-on experience building AI agents or automated workflows with LLMs
Experience running pre- and post-event programs for conferences
Early-stage startup experience, Series A to C
Don’t tick every box? Apply anyway. This role combines skills that rarely sit in one person, so we are looking for strong overlap, not a perfect match.
What We Offer
Compensation: OTE $156,000 – $216,000 USD per year.
Final offer depends on experience and skills.
Remote-first environment
Medical and dental benefits
401(k) plan
Unlimited PTO
Paid holidays
Professional development reimbursement
Location: Argentina
About Improvado
Improvado is an AI-powered marketing data platform built for mid-market and enterprise teams. We help companies automate complex marketing data workflows, unify data at scale, and surface insights through BI and AI. Brands like Genentech, T-Mobile, Activision, and H&R Block rely on us to simplify analytics and improve marketing performance.
We’re a fast-growing Series A startup backed by $34M in funding and driven by a team that moves fast, stays curious, and cares about high-quality execution.
About the role
Improvado is a marketing data platform for enterprise brands and agencies. You will own how our revenue runs and how it is measured. That covers Salesforce and the GTM tools, the path from signed contract to paid invoice, the revenue data model, and the reports leadership decides from. You work closely with the analytics team: you don’t have to build the data models yourself, but you must read them, spot what is wrong and say what to change. You work through AI agents daily, doing the work of a bigger team.
What you will do
Salesforce and GTM stack
Own Salesforce configuration, release process and user access, plus the integrations around it (Gong, Clay, Lemlist, enrichment, e-signature, lead routing).
Keep CRM data clean with guardrails that block mistakes. Manage licences and vendor renewals.
Maintain the Salesforce manual and RevOps SOPs, and train new hires.
Contract to cash
Own ARR and bookings logic (new, upsell, downsell, churn, renewals) and keep agreement, opportunity, and invoice consistent.
Run the closed-deals and billing check with Finance. Fix errors at the source and add a control so they don’t repeat.
Audit invoices, track and chase unpaid ones, and prepare quarterly commission and quota reports.
Revenue analytics and data model
Own the quarterly sales analytics pack, the CRO report, the forecast and pipeline coverage (including CS renewal inputs).
Track funnel conversion, explain why deals move, stall or slip, and keep attribution correct with Marketing.
Query ClickHouse daily, trace any number to its source, and write specs for model changes. Give each metric one documented definition and add data quality checks.
Build leadership dashboards, retire unused ones, and end every analysis with a recommendation, a number and an owner.
AI and automation
Use coding agents (Claude Code, Codex) for queries, scripts and Salesforce automation, and always verify their output.
Automate manual work (invoice and payment checks, CRM hygiene, deal-risk alerts, call analysis) and measure hours saved and errors avoided.
Ownership
Be the single RevOps contact for Sales, CS, Marketing, Finance and Product. Turn each request into a task with an owner, date and success metric.
Requirements
4-6+ years in RevOps, Sales Ops or GTM systems in B2B SaaS, with hands-on admin of a live Salesforce org.
Strong SQL and comfort in a modeled warehouse (layers, lineage, one definition per metric). dbt is a plus.
Deep knowledge of the B2B SaaS revenue cycle: funnel, pipeline, forecast, ARR, churn, renewals.
Experience running the money side: contracts, invoicing, collections, kept in sync with the CRM.
Proof you build with AI agents: something you shipped that others use.
Fluent English and clear written updates without being asked.
Nice to have
Salesforce Administrator or Advanced Administrator certification.
ClickHouse or another columnar warehouse, and any BI tool.
Salesforce development (Apex, triggers, API).
Gong, Clay, Lemlist, or workflow automation tools.
Billing, CPQ, or accounting systems.
HubSpot to Salesforce migration.
Attribution or CS health score data.
Martech or analytics company background.
What We Offer
Remote-first environment
Strong product/market fit: marketing data product for US-based enterprises
20 working days of PTO per year
US holidays and additional days off
Professional development reimbursement
Location: Serbia
About Improvado
Improvado is an AI-powered marketing data platform built for mid-market and enterprise teams. We help companies automate complex marketing data workflows, unify data at scale, and surface insights through BI and AI. Brands like Genentech, T-Mobile, Activision, and H&R Block rely on us to simplify analytics and improve marketing performance.
We’re a fast-growing Series A startup backed by $34M in funding and driven by a team that moves fast, stays curious, and cares about high-quality execution.
About the role
Improvado is a marketing data platform for enterprise brands and agencies. You will own how our revenue runs and how it is measured. That covers Salesforce and the GTM tools, the path from signed contract to paid invoice, the revenue data model, and the reports leadership decides from. You work closely with the analytics team: you don’t have to build the data models yourself, but you must read them, spot what is wrong and say what to change. You work through AI agents daily, doing the work of a bigger team.
What you will do
Salesforce and GTM stack
Own Salesforce configuration, release process and user access, plus the integrations around it (Gong, Clay, Lemlist, enrichment, e-signature, lead routing).
Keep CRM data clean with guardrails that block mistakes. Manage licences and vendor renewals.
Maintain the Salesforce manual and RevOps SOPs, and train new hires.
Contract to cash
Own ARR and bookings logic (new, upsell, downsell, churn, renewals) and keep agreement, opportunity, and invoice consistent.
Run the closed-deals and billing check with Finance. Fix errors at the source and add a control so they don’t repeat.
Audit invoices, track and chase unpaid ones, and prepare quarterly commission and quota reports.
Revenue analytics and data model
Own the quarterly sales analytics pack, the CRO report, the forecast and pipeline coverage (including CS renewal inputs).
Track funnel conversion, explain why deals move, stall or slip, and keep attribution correct with Marketing.
Query ClickHouse daily, trace any number to its source, and write specs for model changes. Give each metric one documented definition and add data quality checks.
Build leadership dashboards, retire unused ones, and end every analysis with a recommendation, a number and an owner.
AI and automation
Use coding agents (Claude Code, Codex) for queries, scripts and Salesforce automation, and always verify their output.
Automate manual work (invoice and payment checks, CRM hygiene, deal-risk alerts, call analysis) and measure hours saved and errors avoided.
Ownership
Be the single RevOps contact for Sales, CS, Marketing, Finance and Product. Turn each request into a task with an owner, date and success metric.
Requirements
4-6+ years in RevOps, Sales Ops or GTM systems in B2B SaaS, with hands-on admin of a live Salesforce org.
Strong SQL and comfort in a modeled warehouse (layers, lineage, one definition per metric). dbt is a plus.
Deep knowledge of the B2B SaaS revenue cycle: funnel, pipeline, forecast, ARR, churn, renewals.
Experience running the money side: contracts, invoicing, collections, kept in sync with the CRM.
Proof you build with AI agents: something you shipped that others use.
Fluent English and clear written updates without being asked.
Nice to have
Salesforce Administrator or Advanced Administrator certification.
ClickHouse or another columnar warehouse, and any BI tool.
Salesforce development (Apex, triggers, API).
Gong, Clay, Lemlist, or workflow automation tools.
Billing, CPQ, or accounting systems.
HubSpot to Salesforce migration.
Attribution or CS health score data.
Martech or analytics company background.
What We Offer
Remote-first environment
Strong product/market fit: marketing data product for US-based enterprises
20 working days of PTO per year
US holidays and additional days off
Professional development reimbursement
Location: Mexico
About Improvado
Improvado is an AI-powered marketing data platform built for mid-market and enterprise teams. We help companies automate complex marketing data workflows, unify data at scale, and surface insights through BI and AI. Brands like Genentech, T-Mobile, Activision, and H&R Block rely on us to simplify analytics and improve marketing performance.
We’re a fast-growing Series A startup backed by $34M in funding and driven by a team that moves fast, stays curious, and cares about high-quality execution.
About the role
Improvado is a marketing data platform for enterprise brands and agencies. You will own how our revenue runs and how it is measured. That covers Salesforce and the GTM tools, the path from signed contract to paid invoice, the revenue data model, and the reports leadership decides from. You work closely with the analytics team: you don’t have to build the data models yourself, but you must read them, spot what is wrong and say what to change. You work through AI agents daily, doing the work of a bigger team.
What you will do
Salesforce and GTM stack
Own Salesforce configuration, release process and user access, plus the integrations around it (Gong, Clay, Lemlist, enrichment, e-signature, lead routing).
Keep CRM data clean with guardrails that block mistakes. Manage licences and vendor renewals.
Maintain the Salesforce manual and RevOps SOPs, and train new hires.
Contract to cash
Own ARR and bookings logic (new, upsell, downsell, churn, renewals) and keep agreement, opportunity, and invoice consistent.
Run the closed-deals and billing check with Finance. Fix errors at the source and add a control so they don’t repeat.
Audit invoices, track and chase unpaid ones, and prepare quarterly commission and quota reports.
Revenue analytics and data model
Own the quarterly sales analytics pack, the CRO report, the forecast and pipeline coverage (including CS renewal inputs).
Track funnel conversion, explain why deals move, stall or slip, and keep attribution correct with Marketing.
Query ClickHouse daily, trace any number to its source, and write specs for model changes. Give each metric one documented definition and add data quality checks.
Build leadership dashboards, retire unused ones, and end every analysis with a recommendation, a number and an owner.
AI and automation
Use coding agents (Claude Code, Codex) for queries, scripts and Salesforce automation, and always verify their output.
Automate manual work (invoice and payment checks, CRM hygiene, deal-risk alerts, call analysis) and measure hours saved and errors avoided.
Ownership
Be the single RevOps contact for Sales, CS, Marketing, Finance and Product. Turn each request into a task with an owner, date and success metric.
Requirements
4-6+ years in RevOps, Sales Ops or GTM systems in B2B SaaS, with hands-on admin of a live Salesforce org.
Strong SQL and comfort in a modeled warehouse (layers, lineage, one definition per metric). dbt is a plus.
Deep knowledge of the B2B SaaS revenue cycle: funnel, pipeline, forecast, ARR, churn, renewals.
Experience running the money side: contracts, invoicing, collections, kept in sync with the CRM.
Proof you build with AI agents: something you shipped that others use.
Fluent English and clear written updates without being asked.
Nice to have
Salesforce Administrator or Advanced Administrator certification.
ClickHouse or another columnar warehouse, and any BI tool.
Salesforce development (Apex, triggers, API).
Gong, Clay, Lemlist, or workflow automation tools.
Billing, CPQ, or accounting systems.
HubSpot to Salesforce migration.
Attribution or CS health score data.
Martech or analytics company background.
What We Offer
Remote-first environment
Strong product/market fit: marketing data product for US-based enterprises
20 working days of PTO per year
US holidays and additional days off
Professional development reimbursement
Location: Brazil
About Improvado
Improvado is an AI-powered marketing data platform built for mid-market and enterprise teams. We help companies automate complex marketing data workflows, unify data at scale, and surface insights through BI and AI. Brands like Genentech, T-Mobile, Activision, and H&R Block rely on us to simplify analytics and improve marketing performance.
We’re a fast-growing Series A startup backed by $34M in funding and driven by a team that moves fast, stays curious, and cares about high-quality execution.
About the role
Improvado is a marketing data platform for enterprise brands and agencies. You will own how our revenue runs and how it is measured. That covers Salesforce and the GTM tools, the path from signed contract to paid invoice, the revenue data model, and the reports leadership decides from. You work closely with the analytics team: you don’t have to build the data models yourself, but you must read them, spot what is wrong and say what to change. You work through AI agents daily, doing the work of a bigger team.
What you will do
Salesforce and GTM stack
Own Salesforce configuration, release process and user access, plus the integrations around it (Gong, Clay, Lemlist, enrichment, e-signature, lead routing).
Keep CRM data clean with guardrails that block mistakes. Manage licences and vendor renewals.
Maintain the Salesforce manual and RevOps SOPs, and train new hires.
Contract to cash
Own ARR and bookings logic (new, upsell, downsell, churn, renewals) and keep agreement, opportunity, and invoice consistent.
Run the closed-deals and billing check with Finance. Fix errors at the source and add a control so they don’t repeat.
Audit invoices, track and chase unpaid ones, and prepare quarterly commission and quota reports.
Revenue analytics and data model
Own the quarterly sales analytics pack, the CRO report, the forecast and pipeline coverage (including CS renewal inputs).
Track funnel conversion, explain why deals move, stall or slip, and keep attribution correct with Marketing.
Query ClickHouse daily, trace any number to its source, and write specs for model changes. Give each metric one documented definition and add data quality checks.
Build leadership dashboards, retire unused ones, and end every analysis with a recommendation, a number and an owner.
AI and automation
Use coding agents (Claude Code, Codex) for queries, scripts and Salesforce automation, and always verify their output.
Automate manual work (invoice and payment checks, CRM hygiene, deal-risk alerts, call analysis) and measure hours saved and errors avoided.
Ownership
Be the single RevOps contact for Sales, CS, Marketing, Finance and Product. Turn each request into a task with an owner, date and success metric.
Requirements
4-6+ years in RevOps, Sales Ops or GTM systems in B2B SaaS, with hands-on admin of a live Salesforce org.
Strong SQL and comfort in a modeled warehouse (layers, lineage, one definition per metric). dbt is a plus.
Deep knowledge of the B2B SaaS revenue cycle: funnel, pipeline, forecast, ARR, churn, renewals.
Experience running the money side: contracts, invoicing, collections, kept in sync with the CRM.
Proof you build with AI agents: something you shipped that others use.
Fluent English and clear written updates without being asked.
Nice to have
Salesforce Administrator or Advanced Administrator certification.
ClickHouse or another columnar warehouse, and any BI tool.
Salesforce development (Apex, triggers, API).
Gong, Clay, Lemlist, or workflow automation tools.
Billing, CPQ, or accounting systems.
HubSpot to Salesforce migration.
Attribution or CS health score data.
Martech or analytics company background.
What We Offer
Remote-first environment
Strong product/market fit: marketing data product for US-based enterprises
20 working days of PTO per year
US holidays and additional days off
Professional development reimbursement
(Multiple States), United States
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