Дата-инженер вакансии

Location: United States Workplace: Remote BUILDING THE FUTURE OF OPEN FINANCE Payward - the parent company behind Kraken, NinjaTrader, Breakout, xStocks, Payward Services and CF Benchmarks - has spent the last 15 years building one of the most modern and globally accessible financial infrastructure platforms in the industry, built to advance an open, global financial system. THE TEAM Founded in 2011, Kraken is one of the world's longest-standing crypto platforms, trusted by over 10 million individuals and institutions across the globe. It offers spot trading, margin, futures, staking, and OTC services, with products built for both individual investors and institutional clients.   We’re looking for a Senior Analytics Engineer to join Kraken’s Finance Data Eng & Analytics team within Data Platform. You’ll build trusted financial data products, using automation and AI to reduce manual work and improve the close process. Working at the intersection of data engineering and accounting, you’ll turn complex financial data into reliable pipelines, models and automated reconciliations. You’ll use AI-assisted tools to accelerate development and investigations, keeping financial outputs tested and traceable.   THE OPPORTUNITY - You’ll work closely with Data Engineers, Finance and Platform Accounting to improve reporting and decision-making. Clear communication and collaboration will help you agree priorities, explain findings and drive adoption. - Partner with Finance to understand workflows, identify pain points and translate business needs into practical data and automation solutions. - Build and maintain automated workflows for data preparation, reconciliations, reporting and follow-ups, with clear controls, monitoring and exception handling. - Build intuitive dashboards and visualizations that help stakeholders understand performance, investigate issues and make decisions. - Resolve finance data issues and reconciliation breaks with Finance, Product and Engineering, addressing root causes and communicating progress. - Build scalable, reliable pipelines to collect, transform and curate data from internal platforms and external systems. - Support the design and rollout of a trusted finance data lake, with clear definitions, ownership and quality checks. - Improve data timeliness, enabling near-real-time insights where they add business value. - Evaluate new technologies, including AI-assisted automation, and prototype solutions to Finance needs that can be reliably supported in production.   WHAT YOU BRING - 7+ years of experience in data analytics and data management within financial services or a regulated industry, working directly with Finance teams. - Strong SQL and Python skills, including writing, debugging and maintaining production code. - Solid experience with Git, merge requests, and code reviews, supported by strong software engineering practices, including testing and documentation. - Hands-on experience building and operating pipelines and models using dbt, Spark, Airflow or comparable tools. - Direct experience with finance or accounting data, such as reconciliations, subledgers, cost basis or month-end close, including investigating discrepancies and validating outputs with accounting stakeholders. - A track record of owning pipelines end to end, making sound technical decisions and improving reliability as requirements and systems evolve. - Judgment and autonomy to lead complex work, navigate ambiguity and remain accountable for production outcomes. - Experience supporting recurring financial close activities, combined with a proactive approach to identifying manual processes and replacing them with reliable, sustainable automation. - Excellent communication skills, with the ability to collaborate across Engineering and Finance, explain complex technical issues, articulate financial implications, and recommend actionable solutions.   NICE TO HAVES - Experience with cryptocurrency transaction data and how digital-asset activity flows into financial records and reconciliations. - Experience processing large volumes of financial transactions using distributed frameworks such as Spark or PySpark. Unless a specific application deadline is stated in the job posting, applications are accepted on an ongoing basis. Please note, applicants are permitted to redact or remove information on their resume that identifies age, date of birth, or dates of attendance at or graduation from an educational institution. We consider qualified applicants with criminal histories for employment on our team, assessing candidates in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance. OUR COMMITMENT Payward is powered by people from around the world and we celebrate the diverse talents, backgrounds, contributions, and unique perspectives that everyone brings to the table. We hire based on merit, seeking out people with the right abilities, knowledge, and skills for the job. We encourage you to apply for roles where you don't fully meet the listed requirements, especially if you're passionate or knowledgeable about crypto. We may ask candidates to complete job-related skills or work-style assessments as part of our hiring process. These assessments evaluate competencies relevant to the role and are applied consistently across candidates for similar positions. Results are considered alongside experience and interviews, and are not the sole basis for any employment decision. As an equal opportunity employer, we don't tolerate discrimination or harassment of any kind, whether based on race, ethnicity, age, gender identity, citizenship, religion, sexual orientation, disability, pregnancy, veteran status, or any other protected characteristic as outlined by federal, state, or local laws. Stay connected Follow us on Twitter https://twitter.com/krakenfx Learn on the Kraken Blog https://blog.kraken.com/#:~:text=Enter%20your%20email%20address Connect on LinkedIn https://www.linkedin.com/company/kraken-exchange/ Candidate Privacy Notice https://www.kraken.com/legal/candidate-privacy-notice

Location: Brazil; Peru; Colombia; Argentina; Canada; Mexico; Chile Workplace: Remote BUILDING THE FUTURE OF OPEN FINANCE Payward - the parent company behind Kraken, NinjaTrader, Breakout, xStocks, Payward Services and CF Benchmarks - has spent the last 15 years building one of the most modern and globally accessible financial infrastructure platforms in the industry, built to advance an open, global financial system. THE TEAM Founded in 2011, Kraken is one of the world's longest-standing crypto platforms, trusted by over 10 million individuals and institutions across the globe. It offers spot trading, margin, futures, staking, and OTC services, with products built for both individual investors and institutional clients.   We’re looking for a Senior Analytics Engineer to join Kraken’s Finance Data Eng & Analytics team within Data Platform. You’ll build trusted financial data products, using automation and AI to reduce manual work and improve the close process. Working at the intersection of data engineering and accounting, you’ll turn complex financial data into reliable pipelines, models and automated reconciliations. You’ll use AI-assisted tools to accelerate development and investigations, keeping financial outputs tested and traceable.   THE OPPORTUNITY - You’ll work closely with Data Engineers, Finance and Platform Accounting to improve reporting and decision-making. Clear communication and collaboration will help you agree priorities, explain findings and drive adoption. - Partner with Finance to understand workflows, identify pain points and translate business needs into practical data and automation solutions. - Build and maintain automated workflows for data preparation, reconciliations, reporting and follow-ups, with clear controls, monitoring and exception handling. - Build intuitive dashboards and visualizations that help stakeholders understand performance, investigate issues and make decisions. - Resolve finance data issues and reconciliation breaks with Finance, Product and Engineering, addressing root causes and communicating progress. - Build scalable, reliable pipelines to collect, transform and curate data from internal platforms and external systems. - Support the design and rollout of a trusted finance data lake, with clear definitions, ownership and quality checks. - Improve data timeliness, enabling near-real-time insights where they add business value. - Evaluate new technologies, including AI-assisted automation, and prototype solutions to Finance needs that can be reliably supported in production.   WHAT YOU BRING - 7+ years of experience in data analytics and data management within financial services or a regulated industry, working directly with Finance teams. - Strong SQL and Python skills, including writing, debugging and maintaining production code. - Solid experience with Git, merge requests, and code reviews, supported by strong software engineering practices, including testing and documentation. - Hands-on experience building and operating pipelines and models using dbt, Spark, Airflow or comparable tools. - Direct experience with finance or accounting data, such as reconciliations, subledgers, cost basis or month-end close, including investigating discrepancies and validating outputs with accounting stakeholders. - A track record of owning pipelines end to end, making sound technical decisions and improving reliability as requirements and systems evolve. - Judgment and autonomy to lead complex work, navigate ambiguity and remain accountable for production outcomes. - Experience supporting recurring financial close activities, combined with a proactive approach to identifying manual processes and replacing them with reliable, sustainable automation. - Excellent communication skills, with the ability to collaborate across Engineering and Finance, explain complex technical issues, articulate financial implications, and recommend actionable solutions.   NICE TO HAVES - Experience with cryptocurrency transaction data and how digital-asset activity flows into financial records and reconciliations. - Experience processing large volumes of financial transactions using distributed frameworks such as Spark or PySpark.       Unless a specific application deadline is stated in the job posting, applications are accepted on an ongoing basis. Please note, applicants are permitted to redact or remove information on their resume that identifies age, date of birth, or dates of attendance at or graduation from an educational institution. We consider qualified applicants with criminal histories for employment on our team, assessing candidates in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance. OUR COMMITMENT Payward is powered by people from around the world and we celebrate the diverse talents, backgrounds, contributions, and unique perspectives that everyone brings to the table. We hire based on merit, seeking out people with the right abilities, knowledge, and skills for the job. We encourage you to apply for roles where you don't fully meet the listed requirements, especially if you're passionate or knowledgeable about crypto. We may ask candidates to complete job-related skills or work-style assessments as part of our hiring process. These assessments evaluate competencies relevant to the role and are applied consistently across candidates for similar positions. Results are considered alongside experience and interviews, and are not the sole basis for any employment decision. As an equal opportunity employer, we don't tolerate discrimination or harassment of any kind, whether based on race, ethnicity, age, gender identity, citizenship, religion, sexual orientation, disability, pregnancy, veteran status, or any other protected characteristic as outlined by federal, state, or local laws. Stay connected Follow us on Twitter https://twitter.com/krakenfx Learn on the Kraken Blog https://blog.kraken.com/#:~:text=Enter%20your%20email%20address Connect on LinkedIn https://www.linkedin.com/company/kraken-exchange/ Candidate Privacy Notice https://www.kraken.com/legal/candidate-privacy-notice

- Миграция ETL-системы телеком-оператора с Oracle/Informatica на Apache стек (S3, Airflow, NiFi, Kafka, Greenplum); - Перенос бизнес-логики из Informatica в высокопроизводительный SQL-код;

- Миграция ETL-системы телеком-оператора с Oracle/Informatica на Apache стек (S3, Airflow, NiFi, Kafka, Greenplum); - Перенос бизнес-логики из Informatica в высокопроизводительный SQL-код;

Сбор данных из внутренних сервисов, LLM Gateway, GitLab, Jira, логов AI-агентов и других источников. Проектирование и наполнение баз данных и аналитических витрин для оценки использования CodeGen-платформы.

-Разрабатывать и развивать ETL-системы, которые обеспечивают данными различные предметные области: продажи, учёт, логистику. -Анализировать данные и проектировать структуры данных под аналитические потребности различных бизнес-подразделений.

Организация пайплайнов потоков данных (конвейера движения данных в компании); Разработка, поддержка и оптимизация производительности Корпоративного Хранилища Данных (EDWH);

разработка и автоматизация пайплайнов репликации данных с Qlik Replicate (CDC, стриминг в Kafka/Redshift); настройка источников (SQL, NoSQL) и целей (data warehouse, облако);

Location: Remote, Global About the Role We're looking for a FinOps Data Engineer to help drive cost optimization and cloud financial governance across our AWS infrastructure and GCP footprint. You'll work cross-functionally with Engineering, Finance, and Product to find and ship savings, build the tooling necessary to maximize efficiency of every dollar spent in the cloud, and cultivate a culture of cost awareness across engineering teams. This role owns the data side of that mission: the models, pipelines, and analysis that make cost, usage, and margin numbers trustworthy across the company. This is a hands-on engineering role, not an analyst seat. You'll do the schlep work other teams don't have time for, digging into a bucket's access pattern, writing the dbt model or Python pipeline that ships the fix, building the cost-vs-revenue mart that answers a margin question once instead of every time, and chasing a number until it reconciles. Strong communication matters as much as technical depth, you'll regularly explain a savings case or a data model's implications to an engineering team, a finance stakeholder, and a leadership audience in the same week. What You'll Be Responsible For In this role, you'll: - Find and ship savings across AWS and GCP: Analyze billing, usage, and cost trends to identify opportunities, rightsizing, storage lifecycle policies, data transfer, and spot/auto-scaling. Take ideas from identification through to a realized dollar figure, not just a recommendation. - Build and maintain the FinOps data pipeline: Design and run the BigQuery/dbt models that turn raw billing and usage data (CUR, Storage Lens) into reconciled, trustworthy mart tables, the foundation everything else in FinOps is built on. - Build cost-vs-revenue and customer segmentation models: Model unit economics and margin by product line and customer segment, and extend coverage as new products and segments come online. - Apply data science to cost and usage data: Build forecasting models, detect anomalies statistically rather than by threshold alone, and quantify savings opportunities (e.g. storage lifecycle, request-cost patterns) with rigor a spreadsheet can't match. - Build and maintain cost attribution: Tagging policies, cost allocation, and chargeback/showback so engineering teams can see and own their own spend. This is foundational work other savings work depends on. - Automate cost governance: Build automated cost reporting, anomaly detection, and alerting. Write the scripts and infrastructure-as-code (Python, Terraform, Lambda) that enforce cost policy rather than relying on manual review. - Partner with engineering on architecture decisions: Bring cost into the room early, cost-efficiencies are cheaper to get right at design than to retrofit. - Work with Finance on forecasting and reporting: Support budget tracking and financial KPIs; build dashboards and self-serve analytics engineering and finance can both read. You Might Be a Good Fit If You - Have strong AWS expertise and working GCP knowledge, including pricing models, billing structures, and cost optimization mechanics. - Have data engineering depth: dbt, BigQuery (or an equivalent modern warehouse), SQL at scale, and data modeling for analytics (dimensional modeling, incremental models, testing). - Have data science fundamentals: statistical forecasting, anomaly detection, and experimentation, and know when a rigorous model beats a rule of thumb. - Have automation and scripting skills: Python, Terraform/ pulumi, Lambda, or Bash. - Are comfortable with cost/billing tooling: AWS Cost Explorer, Cost & Usage Reports, GCP Billing Reports, and BI/dashboarding (we use Hex). - Have experience with, or are comfortable picking up, Linear and Notion as the system of record for tracking and documenting FinOps work. - Experience with a dedicated cloud cost management platform (e.g. CloudZero, Vantage, or similar) is a plus, not required. - Can analyze cost data and present it clearly to both technical and non-technical audiences. - Have prior experience spanning data analytics, data engineering, or data science in a FinOps, cloud cost management, or cloud governance context. - Are energized by doing the unglamorous, high-leverage work (the schlep) that unblocks other teams, and by working cross-functionally without positional authority. WHAT WE OFFER - Fully Remote We hire globally. We believe you can do your best work from anywhere. There are no Supabase offices, but we provide a WeWork membership or co-working allowance you can use anywhere in the world. - ESOP Every team member receives ESOP (equity ownership) in the company. We want everyone to share in the upside of what we’re building together. - Tech Allowance Use this budget to set up your ideal work environment—laptop, monitor, headphones, or whatever helps you do your best work. - Health Benefits Supabase covers 100% of health insurance for employees and 80% for dependents, wherever you are. Your wellbeing and your family’s health are important to us. - Annual Off-Sites Once a year, the entire company gathers in a new city for a week of connection, collaboration, and fun. It’s a highlight of our year. - Flexible Work We operate asynchronously and trust you to manage your own time. You know what needs to be done and when. - Professional Development Every team member receives an annual education allowance to spend on learning—courses, books, conferences, or anything that supports your growth. ABOUT THE TEAM Supabase was born-remote and open-source-first. We believe our globally distributed team is our secret weapon in building tools developers love. - ~400 team members - 60+ countries - 20+ languages spoken - Over $1B raised (including our $500M Series F) - 540,000+ community members We move fast, build in public, and use what we ship. If it’s in your project, we probably use it in ours too. We believe deeply in the open-source ecosystem and strive to support—not replace—existing tools and communities.

Задачи: — вести сбор требований с бизнес‑стейкхолдерами — проектировать модели данных по паттернам medallion‑архитектуры (bronze/silver/gold) — строить и оптимизировать ETL/ELT‑пайплайны с dbt — настраивать проверки качества данных (Great Expectations) — оптимизировать Spark‑джобы под SLA — диагностировать проблемы в многосистемных пайплайнах — поддерживать документацию и runbook’и Требования: — от 5 лет опыта в Data Engineering — продвинутое владение Python, Spark и SQL — опыт работы с AWS и Databricks — знание Kafka и инструментов современного Data‑стека (dbt, Airflow или аналогов) — понимание dimensional‑моделирования (star/snowflake‑схемы) — понимание принципов проектирования хранилищ данных и архитектур (lambda, kappa, lakehouse) — умение структурировать задачи для работы с ИИ‑ассистентами — знание топологий систем — уровень английского — Intermediate и выше.

-Разрабатывать и поддерживать ETL процедуры в Airflow. -Проектировать, реализовывать и внедрять распределённые системы обработки и хранения данных.

• Коммерческий опыт построения и развития DWH и data pipelines. • Уверенный коммерческий опыт минимум с 3 из 4 инструментов: ClickHouse, Debezium, dbt, Airflow.

— Администрировать и поддерживать Percona MySQL PXC Cluster; — Настраивать HAProxy для балансировки нагрузки и высокой доступности;

Location: United States; Canada Workplace: OnSite WHO ARE WE? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! Role Overview As a Member of Technical Staff in Post-Training Data Infrastructure, you will build and own the pipelines that produce the post-training data for our large language models (LLMs), from SFT and RL data to synthetic and human-annotated data. You will work closely with researchers and annotation teams so they can iterate on data as quickly as they iterate on models. Key Responsibilities - Build and maintain data pipelines for SFT, preference, and RL post-training data. - Own dataset quality, versioning, and reproducibility. - Turn research data recipes into production-grade, reusable components. - Scale model inference for data generation and model-based grading. - Design data formats and schemas that keep post-training datasets consistent and efficient to train on. - Collaborate with researchers and engineers to make sure data pipelines keep pace with the needs of cutting-edge language models. Qualifications - Strong software engineering skills. - Experience designing and building large-scale data pipelines for machine learning, ideally for LLM post-training. - Track record of running distributed data processing and model inference at scale. - Good understanding of post-training methods (e.g., SFT, RLHF, DPO) and how data shapes model behaviour. - Excellent communication skills. This is neither an exhaustive nor necessary set of attributes. Even if none of these apply to you, but you believe you will contribute to Cohere, please reach out. We value diverse backgrounds and perspectives at Cohere   FULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS: - A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch. - Full health and dental benefits, including a separate budget for mental health. - RRSP matching, 401K, Pension Scheme. - 100% Parental Leave top-up for up to 6 months, for either parent. - Annual enrichment benefits: Arts & culture, fitness/wellness, quality time, and a workspace improvement credit. Education & learning stipend for conferences, courses, and coaching. - 6 weeks of paid vacation (30 working days!) - Budget for traveling to other offices if you are remote, plus an annual company offsite. HOW AND WHERE WE WORK: - Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon. - For those in the office: a daily lunch program, plenty of snacks, and regular community and social events. - For those not near an office: a co-working benefit so you can work alongside others in your city. - Everyone receives a $500 home office stipend to set up your workspace properly. If any of the above doesn’t line up exactly with your experience, we still encourage you to apply. We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, and we will work together to meet your needs. We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider. Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers https://cohere.com/careers page.

Обязанности: — Ведение делопроизводства редакции (табель, служебная документация и т.п.). — Помощь редакторам в подготовке и сдаче книг. — Работа с базой 1С. — Ведение проектов в 1С. — Внесение данных и изменение в программе учета 1С. — Формирование отчетов из программ учета по запросам руководителей. — Распечатка/пересылка рукописи для рецензента/редактора и др. — Ксерокопирование документов. — Информирование об отказе рукописи. — Подготовка и предоставление комплекта сопроводительных документов для производства рукописи. — Внесение изменения в сопроводительные документы. — Внутренняя сверка макетов, проверка колонтитулов и колонцифр (помощь редакторам). — Составление презентаций для продвижения книг. — Осуществление взаимодействия с другими редакциями и службами. — Сдача допечаток. — Отслеживание информации о запрещенных веществах и лгбт-контента, при необходимости внесение информации в 1С, выходные данные и обложку книги. Требования: — Высшее/Среднее специальное образование (в области издательского дела). — Знание основ полиграфического производства, полиграфических материалов и издательских процессов. — Уверенный пользователь ПК: Word, Excel, PowerPoint, желательно 1С. — Организованность, стрессоустойчивость, многозадачность, коммуникабельность. Условия: — Полная пятидневная рабочая неделя (10.00 - 18.30) в офисе в Москва-Сити, Башня "Империя" (метро и МЦК "Деловой центр", метро Москва-Сити). — После испытательного срока один удаленный день. — Оклад, премирование по системе KPI после испытательного срока. — Соблюдение ТК РФ. — Социальный пакет: добровольное медицинское страхование и страхование выезжающих за рубеж, программы заботы о здоровье, занятия спортом, значительная скидка на книги и другую продукцию издательства. — Обучение и профессиональное развитие, внутренние и внешние курсы и тренинги. — Публичная оценка выдающихся результатов и призы для победителей на корпоративной Премии «Признание». — Планирование карьеры в Издательской группе. — Справедливое вознаграждение и премирование по системе KPI.

— Проектирование архитектуры данных: разработка и описание целевой архитектуры хранилищ (OLAP/OLTP), определение моделей данных, выбор форматов хранения и стратегий партиционирования. — Стриминг и батч-обработка: разработка стриминговых джоб на Apache Flink и батч-процессинга на Apache Spark.

Location: New York; San Francisco Workplace: OnSite ABOUT US: AI NEEDS A NEW INFRASTRUCTURE LAYER. WE'RE BUILDING IT AT MODAL. Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now. Our customers include category-defining companies like Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation. Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience. ABOUT GTM ENGINEERING: GTM Engineering is responsible for the data, business logic, workflows, and agents that enable our Marketing and Sales teams to help customers get the most out of Modal. Like the infrastructure beneath the Magic Kingdom https://en.wikipedia.org/wiki/Disney_utilidor_system, much of our work happens behind the scenes. We build systems that anticipate and deliver what customers need to grow on Modal. We bring a product management, growth engineering, and data science approach to GTM, from centralizing our data platform https://deepline.com/blog/gtm-ai-nyc-chris-modal to building agents for account research https://parallel.ai/blog/case-study-modal. THE ROLE: We’re looking for a GTM Engineer to own our GTM data platform. This role combines analytics engineering with revenue operations: you’ll build the org map, people graph, and data systems that help us discover future customers and support our GTM workflows, partnering closely with our Data team on the shared foundation. WHAT YOU’LL WORK ON: - Build and maintain an org map and people graph connecting companies, labs, teams, and individuals to identify researchers, engineers, and early-stage startups, including people beyond our existing users and CRM - Own identity resolution, enrichment, and CRM schemas so affiliations, roles, and relationships stay accurate, current, and traceable to their sources - Build GTM data models in Snowflake and dbt connecting people, companies, workspaces, workloads, usage, and revenue, giving reps and agents a shared foundation for customer context - Partner with RevOps and Marketing to define and implement metrics, data models, and business rules for routing, territory assignments, compensation, pipeline reporting, and attribution - Partner with Data on usage forecasts and account signals; build reproducible reporting that reconciles actuals, forecasts, human adjustments, and credited attainment - Build reverse ETL pipelines for ad audiences, lifecycle campaigns, CRM enrichment, outbound, and event outreach, preserving engagement history and campaign eligibility - Own data quality and system health, from graph coverage and stale enrichment to failed syncs and historical reporting accuracy - Build the tooling that helps GTM Engineering ship reliably, from development environments, automated tests, and deployments to backups and shared AI agents and skills YOU MAY BE A FIT IF: - You’re fluent in SQL and proficient in Python - You already use AI coding tools and agents to build, debug, and automate your work, and help teammates do the same - You’ve built data models and production pipelines with tools like Snowflake, dbt, Modal, or Hex - You have hands-on experience with CRM architecture, integrations, and revenue or marketing operations - You understand how leads, accounts, opportunities, and revenue move through a business - You have high attention to detail and a habit of testing, documenting, and monitoring the systems you build NICE-TO-HAVE: - Experience with usage-based businesses

— проектировать, разрабатывать и развивать data pipelines; — работать с DWH: моделями данных, слоями, витринами и ETL/ELT-процессами;