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Location: New York; Stockholm; 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. THE ROLE: We’re growing our Data team and are looking for our first few key hires to build self-serve data tools and drive business strategy in the right direction. The mission of the Modal Data team is to make it easy to track company goals, make evidence-backed decisions, and prioritize the right work. We do this via: - Self-serve AI analytics tools (Hex, Snowflake) - Embedding with teams as a “data adviser,” providing strategic analysis and consulting WHAT YOU'LL WORK ON: - Contribute to building the most modern analytics stack https://modal.com/blog/analytics-stack in data today to support AI-driven self-serve analysis, key metrics tracking, and external customer reporting - Influence work on new products like LLM Inference Endpoints https://modal.com/blog/qwen3-8-2-4t-a95b-now-available-on-modal through product analytics tracking - Identify millions of dollars of cost savings and optimization across our tools and financial operations - Write data pipelines that power the operations of our business, such as our cloud compute economics or sales comp - Create foundational datasets that can be used by people and AI tools to answer questions around product use cases, financial reporting, and marketing campaigns YOU MAY BE A FIT IF: - You’re fluent in SQL and proficient in Python - You have professional experience with at least 2 of the following tools: Snowflake, dbt, dlt, Modal, Hex, PostHog - You extend your work beyond just data reporting and into action and impact - You have high attention to detail - You’re an excellent and precise communicator - You’re personable and great at building relationships NICE-TO-HAVE: - Experience with AI products, especially LLM inference and sandboxes - Experience in fin ops, fraud, sales ops, or risk, especially in the context of AI (e.g. token cost optimization) - Project management skills - Strong presence in the data community online and offline

Location: Monterrey, Mexico; Montevideo, Uruguay; Remote.AR, Remote.LATAM-country; Remote.CO, Colombia; Rosario, Remote.LATAM-country We are looking for a Data Engineer with a strong technical background and a deep understanding of the data lifecycle from acquisition and integration to modeling and exploitation. The ideal candidate combines technical excellence with business understanding, capable of translating strategic requirements into scalable data solutions that enable decision-making and product development based on reliable and timely information. This person acts as a connector between data architecture, analytics, and business needs, helping the organization evolve toward a data-driven culture. The Data & Analytics organization plays a critical role in enabling data-driven decisions across the company, ensuring information flows efficiently from its generation to its consumption. The team designs and maintains scalable data pipelines and models that power analytical use cases, operational systems, and digital products across LATAM. Operating in a Google Cloud Platform (GCP) environment, the team works with tools such as BigQuery, Dataform, Python, APIs, and Terraform, following Agile and DataOps best practices to ensure reliability, scalability, and business impact. Design, develop, and maintain scalable, efficient data pipelines supporting both analytical and operational needs. Understand the current (“as-is”) state of data sources and architectures, identifying gaps, inefficiencies, and improvement opportunities. Define and lead a data integration and completeness roadmap, ensuring availability, quality, and traceability of information across domains. Translate business requirements into clear technical specifications, collaborating closely with multidisciplinary teams (product, analytics, technology, and business). Implement and optimize data solutions using tools from the GCP ecosystem: BigQuery, Dataform, Terraform, Git, Python, and APIs. Ensure adherence to best practices in version control, documentation, and monitoring of data processes. Actively participate in data project prioritization and planning, balancing business impact and technical feasibility. Promote data governance and literacy, encouraging data-informed decision-making across teams. Mentor peers and contribute to the continuous improvement of data engineering standards. Advanced proficiency in SQL and BigQuery. Experience working with Dataform or similar data modeling frameworks (e.g., dbt). Strong knowledge of Python for data processing and API integrations. Hands-on experience with Infrastructure as Code (Terraform) and version control systems (Git). Deep understanding of the data lifecycle and its impact on digital products and business decision-making. Ability to communicate effectively with both technical and non-technical stakeholders. Experience designing data roadmaps or contributing to technology evolution plans. Proven ability to work in Agile environments, participating in sprints and cross-functional collaboration. Fluency in Spanish (team language); intermediate English for documentation and international collaboration.

Location: Metn, Lebanon The Company Toters is an on-demand e-commerce and delivery platform and operates a service that enables customers to get anything in their city at the highest level of convenience. At Toters, technology is at the heart of everything we do. We have product teams that are working hard every day to create products that make our customers' lives easier. Our engineers are also continuously creating solutions to make our processes more efficient, all in an effort to get to our customers fast and at the best cost. If you are interested in working in a high growth startup environment, and look to be part of a team that will potentially change the way customers shop in the Middle East, apply now. Role We are looking for a Senior Data Analyst to own end-to-end analytics for our core engines. In this role, you won't just build dashboards, you will partner directly with Business Leaders to define roadmaps, business cases, and design synthetic experiments. You will turn ambiguous data into an actionable strategy that directly impacts customer conversion, driver efficiency, and growth. What You’ll Do Product Partnership & Strategy: Collaborate with Product Managers, Strategy Directors and Business leaders to translate complex business problems into data-driven product features and strategic roadmaps. Experimentation & A/B Testing: Design, implement, and analyze rigorous A/B and multivariate tests to evaluate feature launches, pricing mechanics, and UX changes across our apps. Funnel & Marketplace Optimization: Deep-dive into consumer retention, funnel conversion, courier dispatch dynamics, and merchant metrics to unlock business growth and improve unit economics. Metric Standardization: Define core product KPIs and maintain a single source of truth by building robust data models (data warehouse) and production-grade dashboards (Tableau). Data Quality & Infrastructure: Enforce strict data validation standards, maintain data governance across clickstream/event tracking, and ensure downstream reporting reliability. Technical Leadership: Mentor junior analysts, lead code reviews for analytical SQL scripts, and elevate overall analytics best practices across the team. What We’re Looking For Experience: 4+ years of data analytics experience, ideally in a product-led tech company, hyper-growth startup, or multi-sided marketplace (e.g., delivery, ride-hailing, e-commerce). Advanced SQL Mastery: Recognized expert in writing clean, modular, scalable SQL code optimized for complex datasets. Statistical Rigor & Python/R: Strong understanding of hypothesis testing, causal inference, and statistical modeling using Python or R. BI & Data Modeling: Hands-on experience building centralized dashboards in Tableau and working with modern data stack tools (dbt, Snowflake, BigQuery, Databricks). Product Intuition: Demonstrated track record of leveraging user behavior analytics (event tracking, cohort analysis, funnel drop-off) to directly influence product decisions. Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Economics, or an equivalent quantitative field. Nice to Have Experience working with clickstream tools (Mixpanel, Amplitude, Segment). Hands-on knowledge of data engineering tools or orchestration frameworks (Airflow, dbt). Experience in experimentation tools (Statsig)

Location: Oakland, California, United States, AMER From Fivetran’s founding until now, our mission has remained the same: to make access to data as simple and reliable as electricity. With Fivetran, customer data arrives in their warehouses, canonical and ready to query, with no engineering or maintenance required. We’re proud that more organizations continue to leverage our technology every day to become truly data-driven. About Us Fivetran and dbt Labs are bringing together two industry-leading companies with a shared mission: helping organizations unlock the full value of their data. Together, we’re delivering the data infrastructure layer that helps organizations move, transform, and trust their data — from the moment data moves, through every transformation, to the context teams and AI systems rely on. Fivetran helps organizations automate data movement across the systems, clouds, engines, and tools they rely on. dbt Labs pioneered analytics engineering, helping teams transform data into reliable, governed insights. Together, we support thousands of organizations as they build a trusted foundation for analytics, AI, and better business decisions. As we bring our teams and technology together, we’re building on the strengths of both companies while continuing to deliver the products and experiences our customers know and trust. It’s an exciting time to join us: we’re creating a company with the scale, talent, and technology to help more organizations put their data to work with greater speed, confidence, and impact. ​ During this transition period, you may see references to both Fivetran and dbt Labs throughout our recruiting process as we integrate our teams, systems, and career sites. About the Role Fivetran and dbt Labs are building data pipelines to power the modern data stack for thousands of companies. We’re looking for a Program Manager to serve as the operational backbone of our pricing and monetization strategy in addition to supporting strategic initiatives, as needed. Sitting within Product Program Management and partnering closely with Analytics, Product, Sales, Product Marketing, RevOps, Finance, and Engineering, you'll own the cross-functional execution that turns pricing strategy into well-governed, shipped programs. You'll coordinate launches end to end, track commitments, resolve escalations, and expand the playbook that makes growth at Fivetran repeatable and scalable. This is a high-visibility role for someone who thrives at the intersection of program management, GTM, and product strategy.  You will be driving some of the most impactful business outcomes. This is a full-time, hybrid position based out of our Oakland, CA office. Our hybrid work model offers a blend of remote flexibility and in-person collaboration, including two days in the office each week to connect and build as a team. Technologies You’ll Use Google Workspace(sheets)/Excel, Jira, Looker/Sigma/Hex, Notion, Salesforce and Slack. What You’ll Do • Define and own the operating model and standards for pricing program management — the governance frameworks, templates, and decision rights adopted across Growth & Monetization. • Serve as a trusted advisor to Product and Sales leadership on pricing strategy, influencing roadmap and go-to-market decisions and representing pricing in executive forums. • Lead the most complex, high-stakes, cross-org pricing initiatives end to end — navigating ambiguity, aligning competing stakeholders, and setting precedent for how future pricing programs are run • Track executive pricing deliverables and commitments end to end, including commitments made to the CEO. • Coordinate timelines, dependencies, and launch readiness with the ePMO. • Partner with Product Marketing and RevOps on enablement plans and rollout execution. • Manage pricing-related sales escalations and the cross-functional coordination required to resolve them. • Lead pricing analyses and business requests, turning data into recommendations that inform pricing decisions. • Report on pricing outcomes — such as closed-lost-due-to-pricing and overall pricing impact — and translate results into forward-looking recommendations. Skills We’re Looking For • Proven experience in program management or product operations, ideally supporting pricing, monetization, or GTM initiative in B2B SaaS and with consumption based pricing. • A track record of driving cross-functional execution across Product, Sales, Marketing, and Engineering. • Excellent stakeholder management and executive communication, including tracking and reporting on commitments to senior leadership. • Strong analytical skills, with the ability to interpret pricing and revenue data and translate it into clear insights and recommendations. • Experience establishing operating rhythms, governance processes, and repeatable playbooks. • Familiarity with GTM and RevOps tooling and processes (e.g., Salesforce, CPQ, and BI tools such as Looker). • Highly organized and self-directed, able to manage multiple concurrent programs in a fast-paced environment. Bonus Skills​ -Experience in data processing (ETL, ELT) and/or cloud-based platforms -PMP Certified #LI-HYBRID The compensation range displayed on this job posting reflects the minimum and maximum target for new hire compensation for the target position and level, and may include sales incentives or target bonuses depending on the role.  Our compensation ranges are determined by role, level, and location. Our job titles may span more than one career level. Within the range, individual compensation is determined by additional factors, including job-related skills, experience, relevant education or training, business need, market demands. The compensation range is subject to change and may be modified in the future. Your recruiter can share more about the specific compensation range for your location during the hiring process. Oakland Pay Range $158,284 — $197,855 USD Perks and Benefits 100% employer-paid medical insurance * Generous paid time away from work, including paid vacation or flexible/unlimited vacation depending on role and country, plus paid sick time, inclusive parental leave, paid holidays, including a year-end Global Week of Rest, and volunteer days off. RSU stock grants* Professional development and training opportunities Company virtual happy hours, free food, and fun team-building activities Monthly cell phone stipend Access to an innovative mental health support platform that offers personalized care and resources in areas such as: therapy, coaching, and self-guided mindfulness exercises for all covered employees and their covered dependents. * May vary by country and worker type - please reach out to your recruiter for more information Click here to learn more about Fivetran's Benefits by Region. We’re honored to be valued at over $5.6 billion , but more importantly, we’re proud of our core values of Get Stuck In, Do the Right Thing, and One Team, One Dream . Read about us in Forbes . Fivetran brings together high-quality talent across the globe to make data access as easy and reliable as electricity for our customers. We value and recognize that our customers benefit from having innovative teams made of people from many backgrounds, experiences, and identities. Fivetran promotes diversity, equity, inclusion & belonging through attracting, recruiting, developing, and retaining a diverse workforce, not only because it is the right thing to do, but because it helps us build a world-class company to better serve our customers, our people and our communities. To learn more about Fivetran’s culture and what it’s like to be part of the team, click here and enjoy our video. To learn more about our candidate privacy policy, you can read our statement here . We are committed to ensuring that all candidates have an equal opportunity to participate in our interview process. If you require accommodations at any stage of the process due to a disability, medical condition, or any other circumstance, please don't hesitate to submit your request by filling out this form . We will work with you to provide reasonable accommodations to facilitate your participation and ensure a fair and accessible interview experience. Your request and any information provided will be kept confidential and will not impact your candidacy. We look forward to hearing from you and accommodating your needs to the best of our ability.

Quantori разрабатывает программное обеспечение для биотехнологических и фармацевтических компаний. Среди клиентов компании Pfizer, LGC, The Jackson Laboratory. В компании работает более 500 человек, инвестиции составляют 15 миллионов долларов. Требования: — Опыт работы не менее 6 лет — Знание Python, R, SQL, Spark, dbt — Уровень английского языка не ниже B2 Формат работы: удалённо.

О проекте: Мы строим Customer Intelligence & Personalization Platform для экосистемы бизнесов в Армении. В основе — Golden Record: единый профиль клиента, объединяющий данные банка, продуктов экосистемы и внешних источников. Поверх него строятся Customer 360, сегментация, scoring и персонализированные предложения. Задачи роли: Предстоит спроектировать слой между сырыми данными core banking системы и витринами платформы. Источник слабо документирован: часть значений закодирована, часть бизнес-логики реализована в коде и хранимых процедурах, две внешне одинаковые записи могут быть собраны разными алгоритмами из разных таблиц. Нужен человек, который разберёт этот источник и превратит его в осознанную модель данных: что откуда берётся, что на что влияет, какое поле что означает. Что предстоит делать: — Исследовать core banking систему, legacy- и внешние источники, восстанавливать их семантику. — Идти от целевой витрины к источнику: определять, из каких таблиц и по какой логике собирается каждый атрибут. — Разделять случаи, когда одинаковые по виду данные получены разными путями, и фиксировать правила выбора. — Описывать модель данных и метаданные: сущности, связи, grain, ключи, историчность. — Формировать требования к витринам для сегментации, scoring и рекомендаций. — Писать SQL-исследования и прототипы трансформаций, проверять гипотезы на данных. — Выяснять смысл данных напрямую у сотрудников банка — это постоянная часть работы. О бэкграунде: Важно, какие задачи вы решали — приходили в незнакомую недокументированную систему и разбирались сами, восстанавливали бизнес-смысл данных, строили модель с нуля и доводили исследование до работающей витрины. Если вы работали дата-инженером и значительную часть времени тратили на то, чтобы понять, что лежит в источнике, — это наш профиль. Если получали готовые чистые витрины — скорее нет. AI-native подход: Мы используем AI-native подход и ожидаем того же от кандидата: AI-инструменты применяются для разбора legacy, анализа схем, написания SQL и документирования. Требования: — Сильный SQL: оконные функции, сложные join-цепочки, анализ качества и распределений. — Опыт реверс-инжиниринга плохо документированных источников. — Понимание data modelling: сущности, grain, ключи, историчность, master entities. — Опыт работы с банковскими или fintech-данными (Core banking (особенно ArmSoft) / legacy banking systems, Customer 360) или опыт разбора сырых банковских данных о клиентах и продуктах из плохо документированных систем с целью производства маркетинговой и финансовой аналитики. — Python на уровне исследования данных. — Способность самостоятельно довести задачу «вот сырьё, вот целевая витрина» до результата. — Проактивность: готовность выяснять смысл данных у людей, а не ждать документации. — Git, Linux, опыт работы в закрытых Dev/UAT-контурах. Будет плюсом: — Знание армянского языка — Методологии матчинга и дедупликации — Опыт с Data Vault 2.0 — Опыт с DBT или аналоги — PII, маскирование, RBAC — Опыт в регулируемом on-premise контуре — Экспертиза в ML, ML Ops Условия: — Взаимодействие по ИП — Локация - Армения — Формат работы - гибрид, готовность работать из офиса банка до 3 раз в неделю.

Задачи: – проектирование и разработка слоев данных (Raw, Clean, Enriched/Presentation) с учетом лучших практик моделирования (Data Vault 2.0 / 3NF для хранилища, Star Schema для витрин); – разработка и внедрение кастомных алгоритмов дедупликации и Entity Resolution (Fuzzy Matching) на Python; – формализация бизнес-правил качества данных в код, настройка уровней доверия к источникам и правил выживания (survivorship rules); – внедрение Data Contracts и механизмов карантина (quarantine) для некачественных данных с возможностью их последующего разбора; – настройка автоматизированного тестирования данных (Data Quality checks) в пайплайнах. Требования: – Продвинутый уровень SQL и опыт работы с dbt (или аналогами) для построения слоев данных; – уверенное владение PySpark и/или pandas для обработки больших объемов данных и реализации логики матчинга; – практический опыт применения библиотек нечёткого поиска и связывания записей (Splink, recordlinkage, dedupe, theFuzz); – понимание принципов работы алгоритмов сравнения строк (Левенштейн, Джаро-Винклер, косинусное сходство на TF-IDF/эмбеддингах); – глубокое понимание 2-3NF, Data Vault 2.0 и различий между слоями сырых, очищенных и обогащенных данных; – опыт внедрения инструментов тестирования данных (Great Expectations, Soda, dbt tests, deequ или аналоги); – опыт работы с оркестраторами (Airflow, Dagster или Prefect), уверенное использование Git и базовые навыки CI/CD для дата-пайплайнов.

Задачи: — Проектировать, внедрять и поддерживать DevOps инструменты для data pipeline и data платформ. — Поддерживать и автоматизировать ETL процессы (например, Spark, Airflow). — Создавать и управлять мониторингом и наблюдаемостью качества данных (DBT), data pipeline и систем хранения. — Внедрять и поддерживать инфраструктуру хранения данных на AWS (S3, EC2, сеть). — Интегрировать и поддерживать Project Nessie как транзакционный каталог для Data Lakes с семантикой, похожей на Git. — Тесно сотрудничать с Data Engineers для обеспечения плавного деплоя, версионирования и масштабирования data jobs и pipeline. — Автоматизировать деплой и CI/CD процессы с использованием GitLab CI/CD. — Обеспечивать лучшие практики безопасности при работе с чувствительными данными. — Писать автоматизацию инфраструктуры на Python, Bash, Ansible. — Документировать системы, pipeline и DevOps процессы. Требования: — Опыт 5+ лет в DevOps / IT Ops / DataOps с практическим опытом работы с инфраструктурой, связанной с данными. — Глубокие знания Linux (сеть, настройка ядра, оптимизация производительности). — Практический опыт работы с Airflow, ClickHouse, DBT, Spark и Project Nessie. — Солидные знания AWS сервисов (S3, EC2, IAM, сеть). — Владение скриптингом и автоматизацией (Python, Bash, Ansible). — Опыт работы с CI/CD пайплайнами (GitLab). — Опыт работы с мониторинговыми стеками (Prometheus, Grafana, ELK). — Знакомство с контейнеризацией и оркестрацией (Docker, Kubernetes). — Понимание концепций управления данными. — Сильные коммуникативные навыки и проактивный подход. Плюсом будет: — Опыт работы с потоковой обработкой данных в реальном времени. — Знание инфраструктуры как кода (Terraform). — Знакомство с безопасностью в data engineering (IAM, шифрование, контроль доступа). — Опыт работы с высокопроизводительными системами или в торговой среде.

Задачи: - Проводить проектную коммуникацию с представителями бизнес-заказчика с целью уточнения бизнес-требований - Выявлять и проводить анализ бизнес-требований, потребностей и проблем бизнес-заказчика - Формировать спецификации требований, описание логических моделей, структур данных и проектных решений (хранилища и витрины данных) - Согласовывать проектные решения с архитекторами, разработчиками и аналитиками смежных команд - Проектировать и разрабатывать ETL/ELT процессы, слои хранилища и витрины данных - Разрабатывать и оптимизировать SQL-запросы - Проводить внутреннее тестирование и контроль качества информационных продуктов, реализованных разработчиками - Принимать участие в приёмочных испытаниях информационных продуктов и документации с заказчиками - Обеспечивать контроль качества данных и их целостность Требования: - Высшее образование: техническое, математическое или финансовое - Опыт работы не менее 5 лет - Уверенные знания Python, GIT, Airflow (DAG с декомпозицией задач, зависимостями, сенсорами) - Глубокое знание SQL: оконные функции, процедуры, функции, вложенные запросы, опыт оптимизации запросов - Понимание индексов, партиций, ключей, нормальных форм, SCD - Понимание моделирования данных, построения хранилища, репликации и консистентности данных - Умение рефакторить код и проводить миграцию из одной системы в другую - Знание принципов CI/CD - Знание PL/SQL для Oracle, Postgres, Greenplum или знание иной MPP СУБД (будет плюсом) - Опыт работы с API (будет плюсом) - Знание ООП, DBT, Kafka, Bash, SAS (будет плюсом)

Location: Singapore You will be a practitioner on our telehealth platform 'Intellect', where your role is to support our clients' mental health and personal growth based on the individual needs and goals. This is delivered primarily through scheduled 60-minute video sessions on our platform. This is a freelance remote role with the flexibility to decide your business hours. Local candidates are preferred but others can still be considered. This role is a great opportunity to supplement your current job or private practice. We're seeking to partner with Clinical Psychologists and Counsellors to be part of a community working collaboratively to bring greater mental health access to the public to provide mental health coaching. What You'll Do Provide 1-on-1 telehealth/or onsite mental health support for Intellect’s clients Focus on clinical cases such as depression, anxiety, trauma, schizophrenia, eating disorders, addiction etc. Collaboratively work with the internal clinical team by suggesting improvements to the program and the platform Requirements Has a Master / Postgraduate Diploma training in Clinical Psychology Has experience working with psychological topics like depression, OCD, PTSD, and the like Skilled in Cognitive Behavioural Therapy or other evidence-based approaches (e.g. Motivational Interviewing, DBT, SFBT, etc.) Candidate must have at least 300 clinical hours post Master's Able to do occasional onsite sessions Candidate must be proficient in English (plus points for other languages) Prior experience in EAP, and/or counselling working is a plus

Location: United Kingdom, London Why Sony Interactive Entertainment? Sony Interactive Entertainment isn’t just the Best Place to Play — it’s also the Best Place to Work. Sony Interactive Entertainment (SIE) is the company behind the PlayStation brand. As a subsidiary of Sony Group Corporation, we’re part of a proud legacy of innovation and excellence. SIE is a dynamic technology company, delivering cutting-edge hardware and network services to more than 100 million people and an entertainment leader, home to some of the most beloved and recognizable intellectual properties (IP) in the world. Our role at SIE is to create and nurture the experiences under the PlayStation brand, a name synonymous with entertainment excellence and creativity. Role Overview: We are seeking a Senior Director to lead our Enterprise Reporting and Analytics Engineering organization, a team of analytics engineers, report developers, visualization specialists, and people leaders. This organization focuses on the last mile of the enterprise data supply chain: the semantic models, curated data products, metric definitions, and consumption experiences that turn engineered data into decisions. This is not a traditional reporting leadership role; the classic notion of “reporting” in the form of a myriad of dashboards and filters is racing towards obsolescence. But the need for data and insights, and the need to deliver it in a way that is digestible and actionable, is timeless. Yes, governed dashboards and trusted reporting remain the foundation, and this leader must be excellent at that foundation. But the mandate is to move the organization decisively beyond static reporting toward a proactive, intelligent analytics capability: partnering with Data Science to productize and visualize their models, enabling generative AI and LLM-based access to our data, building exception-based systems that alert users when outcomes deviate from expectation in a statistically meaningful way, and designing agents that monitor data continuously and deliver insight without being asked. Analytics engineering shares much of its DNA with data engineering — modeling, transformation, testing, version control, CI/CD, performance and cost discipline — but is oriented toward business enablement rather than platform and pipeline. Success in this role therefore depends as much on partnership as on technical depth. This leader will work shoulder to shoulder with Data Engineering on the boundary between platform and consumption, with Product Management on roadmap and requirements, with Data Science on advanced analytic products, and with Analytics Operations on a disciplined intake and prioritization process that makes the best possible use of finite capacity. The ideal candidate has spent years building the traditional foundations — governance, metadata, lineage, dimensional modeling, engaging with enterprise BI platforms— and is now looking to apply that rigor to a fundamentally different generation of analytic products in new and innovative ways. What you'll be doing: Organizational Leadership Lead, coach, and develop an organization of approximately 30 people, including managing through frontline managers; own hiring, role clarity, career pathing, performance management, and succession planning. Define a multi-year vision and roadmap for enterprise reporting and analytics engineering, and translate it into quarterly outcomes the team and its partners can measure. Partner closely with Product Management to jointly shape the multi-year enterprise end-to-end data strategy. Own the organization's budget, vendor relationships, and contractor or offshore capacity. Establish and sustain an engineering culture within an analytics function: peer review, automated testing, documentation standards, source control, CI/CD, etc. Analytics Engineering and Data Architecture Drive maturity of the last-mile architecture in partnership with Data Engineering: curated marts, semantic layers, reusable data products, and certified datasets that serve reporting, ai enablement, data science, and downstream applications. Define and enforce dimensional modeling standards, transformation frameworks, and modular, tested, version-controlled analytics code. Own the enterprise metric layer so that key business measures carry a single, governed definition regardless of where they are consumed. Partner with Data Engineering to define clear contracts and handoffs between pipeline and platform work and last-mile modeling, including shared tooling, standards, and escalation paths. Manage query performance, warehouse consumption, and platform cost as first-class engineering concerns. Data Governance, Metadata, and Lineage Own cataloging, business glossary, data certification, and stewardship workflows, leveraging tools such as Atlan as the primary metadata platform. Maintain column-level lineage across the analytics estate to support impact analysis, change management, audit, and root-cause investigation. Leverage data access provisioning and entitlement models, including row- and column-level security, in partnership with Security, Privacy, and Compliance. Collaborate with Data Engineering to drive data quality monitoring, freshness and availability SLAs, observability, and incident response for analytic assets. Enterprise Reporting and Data Visualization Drive analytics engagement by leveraging enterprise visualization and provisioning platforms, including Domo and Tableau, in partnership with Data Engineering. Ensure best practices are followed in data architecture, governance, adoption, performance, and total cost management. Set visualization and information design standards that make reports readable, consistent, accessible, and decision-oriented. Rationalize the existing reporting portfolio: retire redundant and unused assets, consolidate overlapping content, drive down tech debt and drive consumption toward certified sources. Build a durable self-service capability through training, templates, community, office hours, and clear guardrails on what belongs in self-service versus centrally managed content. Advanced, Proactive, and AI-Enabled Analytics Partner with Data Science to productize their work: build the visualization, interaction, monitoring, and feedback loops that turn models and research into products the business actually uses. Partner with Product Management and Data Engineering to enable generative AI and LLM access to enterprise data. This includes preparing the semantic and metadata foundation that makes data legible to models, delivering conversational analytics, text-to-SQL, and retrieval-augmented experiences on governed sources, and establishing guardrails, evaluation, and accuracy monitoring so that answers can be trusted. Build exception-based analytics that detect when an outcome deviates from its expected value in a statistically significant way, using seasonality-aware baselines, forecast residuals, control limits, and anomaly detection rather than static thresholds. Route those exceptions to accountable owners with context, likely drivers, and a recommended next action; actively tune sensitivity and volume to prevent alert fatigue and preserve signal. Design and deploy analytic agents that proactively monitor data, investigate variances, assemble narrative explanations, and deliver insight into the tools where people already work. Shift the organization's consumption model from pull to push: the measure of success is not how many people opened a dashboard, but whether the right person was told the right thing at the right time. Cross-Functional Partnership, Intake, and Prioritization Operate as a true partner to Product Management: contribute to roadmap, requirements, user research, and release planning, and run analytics with a product mindset covering personas, adoption metrics, and asset lifecycle management. Partner with Analytics Operations to run a transparent intake, triage, sizing, and prioritization process, with published capacity, explicit tradeoffs, and reliable delivery commitments. Make capacity constraints visible to stakeholders and executives, drive reuse over one-off builds, and decline or defer work with evidence rather than friction. Serve as a senior, executive-facing partner to business functions, translating ambiguous business questions into well-scoped analytic solutions. What we're looking for: Bachelor's degree in Computer Science, Information Systems, Engineering, Statistics, Mathematics, Economics, or a related quantitative field; equivalent professional experience will be considered. 15+ years of progressive experience in data, analytics, or business intelligence, including 8+ years leading teams and 4+ years managing managers. Demonstrated experience leading an organization of 25 or more people in a large, matrixed enterprise. Strong hands-on foundation in SQL, dimensional modeling, and ELT/ETL design, with production experience on a modern cloud data warehouse or lakehouse (for example Snowflake, Databricks, BigQuery, Redshift, or Synapse) and a transformation framework such as dbt. Experience applying software engineering practices to analytics work, including Git-based workflows, automated testing, and CI/CD. Enterprise-scale leveraging of BI and data visualization platforms such as Domo, Tableau, Power BI, or Looker, including governance, provisioning, adoption, and cost management. Demonstrated ability to participate in driving data governance program covering catalog, lineage, glossary, stewardship, quality, and access, using platforms such as Atlan, Collibra, Alation, or Informatica. Proven track record of delivering jointly with Data Engineering and Product Management, with clear ownership boundaries and shared accountability. Direct experience partnering with Data Science, including operationalizing, visualizing, and monitoring model output for business consumption. Working command of the statistical concepts underlying exception detection, including hypothesis testing, confidence intervals, variance and control limits, seasonality, and forecasting. Experience running a formal intake, prioritization, and capacity planning process for a shared services or platform organization. Excellent executive communication and influence skills, with the ability to operate effectively amid ambiguity and competing priorities. Experience managing budget, vendor contracts, and platform licensing. Preferred Skills: Master's degree or MBA in a related field. Production experience delivering LLM-based analytics: retrieval-augmented generation over governed data, text-to-SQL or conversational BI, semantic layers designed for model consumption, and prompt, evaluation, and cost monitoring frameworks. Experience designing agentic workflows and orchestration for monitoring, investigation, and notification use cases. Hands-on experience with Atlan, Domo, and Tableau specifically. Proficiency in Python or R for prototyping, and familiarity with common statistical and ML libraries and MLOps concepts. Experience deploying anomaly detection at scale, including alert routing, suppression, and fatigue management. Familiarity with data product and data mesh operating models, domain-aligned ownership, and data contracts. Experience operating in an Agile or product operating model at scale, including portfolio, OKR, and roadmap management. Deep experience with the “Silicon Valley” school of Product Management, as exemplified by the “Marty Kagan”-style academic approach a significant plus. Experience leading global or matrixed teams, including offshore or vendor-partnered delivery models. Track record leading a major platform migration, consolidation, or reporting rationalization effort. Demonstrated success attracting, developing, and retaining senior technical talent in a competitive market. Please note, Sony Interactive Entertainment conducts background checks at the offer stage for all new employees (which may include criminal background checks for some roles) and will need to process personal information to support these checks. Please refer to our Candidate Privacy Notice (where applicable) for more information about what personal information we collect, how we use it, who we share it with, and your data protection rights. Equal Opportunity Statement: Sony is an Equal Opportunity Employer. All persons will receive consideration for employment without regard to gender (including gender identity, gender expression and gender reassignment), race (including colour, nationality, ethnic or national origin), religion or belief, marital or civil partnership status, disability, age, sexual orientation, pregnancy, maternity or parental status, trade union membership or membership in any other legally protected category. We strive to create an inclusive environment, empower employees and embrace diversity. We encourage everyone to respond. Sony Interactive Entertainment is a Fair Chance employer and qualified applicants with arrest and conviction records will be considered for employment.

Location: Indianapolis, United States; Chicago, United States; Cincinnati, United States; Columbus, United States; West Lafayette, United States; Bloomington, United States Location: Indianapolis, IN Metro (Hybrid / 3-Day Onsite) (Open to Regional/EST Candidates with Onsite Travel) Contract Type: Contractor Full-Time / Enterprise Project Engagement (Outsourced via Xenon7) About Xenon7 Where elite tech talent meets world-class opportunities! At Xenon7, we work with leading enterprise clients and innovative startups on high-impact projects across Data, AI, Cloud, and Software Engineering. Our expertise in AI solution architecture and specialized technical talent allows us to partner with enterprise leaders on transformative initiatives, driving innovation and business growth. Job Summary We are seeking a Senior Data Engineer with extensive experience in data architecture and platform engineering to drive data initiatives for a top-tier life sciences client. This role sits at the critical intersection of scientific research informatics, clinical data systems, and manufacturing process engineering. In this position, you will own the architectural vision and hands-on execution of scalable data platforms and pipelines. You will bridge complex data domains—from small and large molecule research, genomics, and clinical trial datasets to active pharmaceutical ingredient (API) manufacturing processes, batch data, and industrial control systems. Operating in a 3-day onsite hybrid capacity in Indianapolis, you will collaborate directly with process engineers, clinical scientists, and platform teams to build high-performance data infrastructure that accelerates drug discovery and manufacturing operations. Key Responsibilities Scientific & Clinical Data Platform Architecture Design, build, and maintain production-grade data pipelines and architecture tailored for scientific, clinical trial, and research informatics data (small/large molecule, genomics, proteomics, LIMS). Structure complex, multi-modal clinical and scientific datasets to enable advanced analytics, enterprise reporting, and downstream machine learning models. Process Engineering & Manufacturing Integration Ingest, harmonize, and model operational technology (OT) and manufacturing process datasets, including API manufacturing pipelines, batch processing data, MES, SCADA, and OSIsoft PI systems. Unify disparate laboratory and facility data pipelines into centralized, highly available enterprise data platforms. Enterprise Data Engineering & Compliance Build robust ETL/ELT pipelines using modern cloud platforms (Databricks, Snowflake, AWS/Azure), PySpark, and SQL. Ensure all data pipelines and platform integrations strictly adhere to enterprise governance, data residency, and GxP regulatory standards within a heavily monitored environment. Technical Leadership & Domain Alignment Partner directly with process engineers, chemical engineering leads, and research informatics directors to translate operational friction into robust technical specifications. Establish engineering best practices, data modeling standards, and pipeline monitoring frameworks across the enterprise data stack. Requirements Experience & Mindset Experience: Senior-level proficiency (10–20+ years) in software development, data platform architecture, and complex ETL/ELT engineering. Domain Adaptability: Demonstrated ability to engineer data pipelines across non-standard, highly specialized domains (e.g., transition between process/chemical engineering data and clinical/scientific research informatics). Location & Work Auth: Must hold unrestricted US Work Authorization (no sponsorship available) and be able to work 3 days per week onsite in the Indianapolis, IN area. Culture & Communication: Exceptional problem-solving mindset, strong adaptability, and the ability to articulate complex technical architecture to cross-functional engineering teams. Must-Have Technical Stack Languages & Frameworks: Advanced Python, PySpark, and expert-level SQL. Data Platforms: Hands-on expertise with Databricks, Snowflake, or AWS/Azure enterprise data ecosystems. Orchestration & ETL: Extensive experience with Airflow, dbt, Spark, and enterprise data orchestration engines. Data Pipelines: Proven track record building streaming and batch data architectures via REST APIs, message brokers, and database integrations. Domain Competency (Scientific & Process Focus) Deep exposure to either scientific/clinical informatics (CDISC/SDTM, LIMS, clinical trials, multi-omics) OR chemical/process engineering data (API manufacturing, batch data, SCADA, MES, OSIsoft PI). Nice-to-Haves & Certifications Academic background in Chemical Engineering, Bio-process Engineering, Computer Science, or a related STEM discipline. Direct experience working inside regulated GxP environments in the Life Sciences or Specialty Chemicals sectors. Certifications: Databricks Certified Data Engineer Senior/Professional, Snowflake SnowPro Core/Advanced, or AWS Data Engineer Associate/Professional. What This Role Is NOT Not a pure Data Scientist or ML Researcher: You will not be building or training machine learning models; you are designing and scaling the underlying data architecture, pipelines, and platform infrastructure. Not a non-coding Architect: This is a 100% hands-on engineering lead role requiring direct pipeline construction and technical execution. Not a Fully Remote Position: This role requires a steady hybrid commitment of 3 days onsite per week at the client site in Indianapolis.

О проекте: Мы строим Customer Intelligence & Personalization Platform для зарубежной экосистемы бизнесов. Задачи: — Принимать технические решения и самостоятельно реализовывать ключевые части платформы. — Владеть архитектурой и развитием Data Core. — Проектировать слои Bronze / Silver / Gold и модели данных. — Исследовать банковские, legacy- и внешние источники, восстанавливать их семантику. — Строить pipelines от raw-данных до подготовленных data products. — Разрабатывать трансформации на SQL / dbt / Python. — Определять инженерные стандарты для ELT, data modelling, metadata, lineage и data quality. — Развивать data catalog и metadata-as-code подход. — Проектировать data contracts и интеграции Data Core с другими компонентами. — Переводить исследовательские решения и ручные процессы в production. — Работать с Git, CI/CD, Docker, Linux и закрытыми Dev/UAT-контурами. — Взаимодействовать с инженерами и data scientist в команде. Требования: — Опыт работы Lead / Staff / Principal Data Engineer или сильного Senior с архитектурным ownership. — Опыт проектирования и самостоятельной реализации data platform / DWH / Lakehouse. — Знания Core banking / legacy banking systems. — Опыт с Customer 360 / Golden Record / MDM. — Сильный SQL и уверенный Python. — Опыт работы с dbt или аналогичными transformation frameworks. — Опыт работы с Airflow / Dagster / Prefect или аналогами. — Хорошее понимание data modelling, historization, grain и master entities. — Практический опыт data quality, lineage и metadata management. — Знание Data Vault 2.0. — Опыт работы с OpenMetadata или другими data catalog solutions. — Умение самостоятельно принимать технические решения и отвечать за результат. — Опыт работы с плохо документированными источниками. — Навыки работы с Git, CI/CD, Docker, Linux. — Практический AI-native способ разработки. Условия: — Оформление: ИП. — Формат работы: полная занятость. — Локация: Ереван либо готовность к регулярным командировкам (до 2 раз в месяц на несколько дней) в Ереван для работы с данными и специалистами заказчика. — Уровень дохода обсуждается индивидуально.

About LawnStarter LawnStarter is the nation's leading on-demand marketplace for lawn care and outdoor services, with over $100M in annual bookings. The company is expanding beyond lawn care to become a one-stop shop for all home services, operating across three brands (LawnStarter, Lawn Love, Home Gnome) on a single shared platform. About Analytics at LawnStarter The analytics team supports the entire company including product, marketing, operations, and finance. The data platform includes a centralized Redshift data warehouse, modeled in dbt and orchestrated by Airflow, with Segment feeding event data. The team is mid-migration to Lightdash as the single BI platform, replacing Tableau and Metabase. Currently, data quality, tracking standards, and platform hygiene are side tasks handled by analysts. The Role The Analytics Engineering Manager will be the first dedicated person for data governance, responsible for data trustworthiness and the roadmap to improve it quarterly. Responsibilities include ensuring quality and freshness of source data, pipelines, reports, metric definitions, Segment event tracking standards, Lightdash workspace health, data feeding ML models, and data security. The role involves collaborating with product, marketing, ops, and finance to prioritize platform work. It is a hands-on role starting solo, building automation, checks, fixing issues, and establishing scalable processes. Eventually, the manager will hire a Lead Analytics Engineer and grow the function as needed. Key differentiators of the role: - First dedicated governance role, reshaping existing standards. - Owns the data roadmap, discovering business needs and prioritizing work. - Whole-stack ownership from source data to ML models. - Leading the migration to Lightdash, setting up permissions and norms. Responsibilities: - Develop and manage the data roadmap balancing business needs and platform health. - Automate monitoring of data quality and freshness, resolve incidents. - Maintain data lineage and impact analysis to assess downstream effects of changes. - Administer Lightdash workspace, permissions, and rollout. - Extend and guard the semantic layer with governed metric definitions. - Govern Segment event tracking catalog, standards, and drift detection. - Ensure AI data readiness for internal AI tools querying the warehouse. - Manage data security, privacy, access controls, and compliance with US state laws. - Maintain governance documentation, ownership models, and review processes. Challenges to solve: - Convert business requests into a prioritized data roadmap. - Ensure Lightdash migration improves platform usability without dashboard sprawl. - Complete and maintain a trustworthy semantic layer. - Manage event-tracking standards and prevent entropy. - Proactively detect and prevent data pipeline breakages with automation and lineage. Success metrics for year 1: - Zero pipeline incidents from unannounced data changes. - Zero freshness incidents; stakeholders never see stale dashboards. - All business areas use official, well-maintained metrics and dashboards. - Every Segment event has an owner and meets standards. - Governance operates as a documented, sustainable system. Requirements: - Passion for data governance as a craft. - Daily use of AI tools for automation, anomaly triage, and documentation. - Hands-on manager who codes, debugs, and configures personally. - Product-minded, able to translate vague requests into prioritized plans. - Automation-first approach to quality checks. - Ability to enforce standards with clear rules and good tooling. Role exclusions: - Not a large-team leadership role; starts solo with potential to hire one lead. - Not a policy or committee role; hands-on fixing with code and conversations. - Not a BI analyst role; focuses on platform and guardrails, not dashboard building. - Not a mature system babysitting role; involves building and evolving the platform. Technologies involved: - Warehouse & pipelines: Redshift, dbt, Airflow - Ingestion: Fivetran, custom Airflow pipelines - Event tracking: Segment - BI tools: Lightdash (primary), Tableau and Metabase (sunsetting) - AI tooling: Claude Code, Codex, Brain (internal AI toolkit) - Observability: AI-powered Analytics Engineer agent for monitoring and anomaly detection Benefits: - Base salary $75k-$120k/year - Fully remote with asynchronous collaboration - Flexible PTO Equal employment opportunity statement included.

Задачи: — Владение дорожной картой данных: выявление потребностей продукта, маркетинга, операций и финансов в данных, приоритизация задач с учетом состояния платформы и последовательное планирование инвестиций. — Обеспечение качества и актуальности данных: автоматический мониторинг источников данных, конвейеров и отчетов, выявление изменений в схемах и источниках до возникновения проблем, управление инцидентами. — Анализ происхождения данных и влияния изменений: создание живой карты от источника до отчетов, оценка влияния изменений на downstream компоненты до их внедрения, внедрение контрактов данных с инженерией. — Администрирование Lightdash: настройка структуры рабочего пространства, прав доступа, обучение пользователей, поддержание порядка и производительности. — Управление семантическим слоем: расширение и поддержка единого определения метрик, предотвращение неконтролируемого роста. — Управление каталогом событий Segment: проверка новых событий на соответствие стандартам, поддержание соответствия с продакшеном, развитие правил и обнаружение отклонений. — Обеспечение готовности данных для AI: контроль доступа AI-инструментов к данным, поддержание документации, консистентности и безопасности. — Обеспечение безопасности и конфиденциальности данных: контроль доступа, обработка персональных данных в соответствии с законами штатов США, регулярные проверки. — Поддержка системы управления: документация, модели владения и циклы обзора для устойчивой работы процессов. Требования: — Профессиональное отношение к управлению данными, стремление к поддержанию чистоты и надежности систем. — Активное использование AI-инструментов для автоматизации проверок, обработки аномалий и документации. — Практический опыт управления командой и самостоятельной работы с SQL, Airflow, настройкой прав доступа. — Ориентация на продукт, умение превращать запросы бизнеса в приоритетный план. — Стремление к автоматизации повторяющихся проверок. — Умение устанавливать стандарты и добиваться их соблюдения с помощью инструментов и коммуникации. Условия: — Базовая зарплата $75,000-$120,000 в год. — Полностью удаленная работа с асинхронным взаимодействием. — Гибкий отпуск по результатам работы. Описание роли: — Первая позиция, посвященная управлению данными и их качеством в компании. — Роль с полным владением цепочкой данных от источника до отчетов и моделей машинного обучения. — Роль с практической деятельностью и последующим расширением команды. — Не руководящая большая команда, а построение и развитие функции. — Не роль BI-аналитика или политика, а инженерно-управленческая позиция. Технологии: — Хранилище и конвейеры: Redshift, dbt, Airflow. — Интеграция данных: Fivetran, кастомные Airflow конвейеры. — Отслеживание событий: Segment. — BI-платформы: Lightdash (основная), Tableau и Metabase (выводятся). — AI-инструменты: Claude Code, Codex, Brain (внутренний AI-инструмент). — Наблюдение: AI-агент для мониторинга свежести, обнаружения аномалий и lineage dbt.

Location: Los Angeles, USA Riot’s Enterprise Technology organization ensures Rioters have what they need to unlock their full potential, from efficient business platforms to next-generation AI capabilities. As a Staff Data and AI Engineer on the Enterprise Data & AI team, you will build the governed data foundations that power enterprise analytics, automation, and applied AI. Your primary focus will be data architecture and data engineering, where you will be responsible for designing and operating production data pipelines in Databricks, integrating & curating data through a medallion architecture, and defining semantic and domain models in Unity Catalog with subject-matter experts. You will also build agents and retrieval-augmented applications directly on this governed data using Databricks capabilities such as Unity Catalog functions, Mosaic AI, and model serving. You will own your systems from architecture through production operations, applying strong engineering, data quality, security, evaluation, and observability practices throughout The ideal candidate is a hands-on data and software engineer who can turn ambiguous enterprise needs into reliable data products and focused AI-enabled solutions. The role will report to the Senior Manager, Enterprise Data and AI. Responsibilities: Data Pipelines and Semantic Layer (Primary) Build and operate pipelines that ingest source-system data into Databricks using a medallion architecture across bronze, silver, and gold layers, with lineage, provenance, access controls, and logging. Partner with subject-matter experts to define and evolve domain, ontology, and semantic models in Unity Catalog, then curate reliable data products into those models. Own data quality for the pipelines and products you build, implementing tests, monitoring, and alerting so issues surface before they reach downstream consumers. Integrate enterprise systems through APIs, events, and middleware while applying appropriate authentication, retries, reconciliation, and auditability. Agents on the Databricks Data Layer (Secondary) Build production agents and retrieval-augmented applications grounded in governed Databricks data, using Unity Catalog functions as tools and Mosaic AI or model-serving capabilities. Add authorization, approvals, and auditability for consequential actions performed through agents or AI-enabled orchestrations. Create evaluation sets and observability for the agents you ship, including groundedness, latency, cost, and production feedback, and use the results to improve them. Engineering Fundamentals Apply solid engineering practices, including automated testing, code review, CI/CD, documentation, on-call support, and concise design notes. Own architecture decisions for your systems and communicate material tradeoffs clearly to technical and business partners. Required Qualifications: Bachelor’s degree in a related field, or equivalent practical experience. 8+ years of experience in data engineering, software or application engineering, or an adjacent enterprise technology role. Hands-on experience building production data pipelines, including ingestion, semantic or domain modeling, data quality, and lineage. Hands-on Databricks experience, including Unity Catalog, medallion architecture, and Mosaic AI or model serving. Hands-on experience shipping production AI solutions, such as retrieval-augmented, conversational, or agentic applications, grounded in data pipelines you built. Proficiency in Python and strong software engineering fundamentals, including testing, code review, CI/CD, deployment, and production support. Experience integrating enterprise systems through APIs, events, or middleware, including authentication, retries, reconciliation, and auditability. Experience with an LLM or cloud AI platform such as Vertex AI, Anthropic, or OpenAI and an automation platform such as Workato, n8n, Camunda, or similar. Working knowledge of data governance, privacy, and security practices applied to production data pipelines and products. Desired Qualifications: Experience with the Mosaic AI Agent Framework or Unity Catalog functions used as agent tools. Experience with enterprise or federated search, vector databases, or permission-aware retrieval. Experience with agent frameworks such as LangGraph, Pydantic AI, LangChain, or CrewAI. Experience with LLM or agent evaluation and observability, such as MLflow tracing. Experience with dimensional modeling or master data management. Experience integrating Workday, ServiceNow, Coupa, Concur, Oracle, OneStream, or Ironclad. Experience with GCP or AWS data services, dbt, containers, or serverless computing. For this role, you’ll find success through craft expertise, a collaborative spirit, and decision-making that prioritizes your fellow Rioters, who are the customers of your work. Being a dedicated fan of games is not necessary for this position. Our Perks: Riot focuses on work/life balance, shown by our open paid time off policy and other perks such as flexible work schedules. We offer medical, dental, and life insurance, parental leave for you, your spouse/domestic partner, and children, and a 401k with company match. Check out our benefits pages for more information. At Riot Games, we put players first . That mission drives every decision in our quest to create games and experiences that make it better to be a player. Whether you’re working directly on a new player-facing experience or you’re supporting the company as a whole, everyone at Riot is part of our mission. And just like in our games, we’re better when we work together. Our goal is to create collaborative teams where you are empowered to bring your unique perspective everyday. If that sounds like the kind of place you want to work, we’re looking forward to your application. It’s our policy to provide equal employment opportunity for all applicants and members of Riot Games, Inc. Riot Games makes reasonable accommodations for handicapped and disabled Rioters and does not unlawfully discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, handicap, veteran status, marital status, criminal history, or any other category protected by applicable federal and state law. We consider for employment all qualified applicants, including those with criminal histories, in a manner consistent with applicable federal, state and local law, including the California Fair Chance Act, the City of Los Angeles Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, the San Francisco Fair Chance Ordinance, and the Washington Fair Chance Act. Per the Los Angeles County Fair Chance Ordinance, the following core duties may create a basis for disqualifying candidates with relevant criminal histories: Safeguarding confidential and sensitive Company data Communication with others, including Rioters and third parties such as vendors, and/or players, including minors Accessing Company assets, secure digital systems, and networks Ensuring a safe interactive environment for players and other Rioters These duties are directly related to essential operations, safety, trust, and compliance obligations within our organization. Please note that job duties may evolve based on business needs and additional responsibilities may be assigned as necessary to maintain operational efficiency and security.

Задачи: — Проектирование, внедрение и поддержка DevOps-инструментов для дата-пайплайнов и платформ. — Поддержка и автоматизация ETL-воркфлоу (например, Spark, Airflow). — Создание и управление мониторингом и наблюдаемостью качества данных (DBT), дата-пайплайнов и систем хранения. — Внедрение и поддержка инфраструктуры хранения данных на AWS (S3, EC2, сеть). — Интеграция и поддержка Project Nessie как транзакционного каталога для Data Lakes с семантикой, похожей на Git. — Тесное взаимодействие с Data Engineers для обеспечения плавного деплоя, версионирования и масштабирования дата-задач и пайплайнов. — Автоматизация деплоя и CI/CD процессов с использованием GitLab CI/CD. — Обеспечение лучших практик безопасности при работе с чувствительными данными. — Написание автоматизации инфраструктуры на Python, Bash, Ansible. — Документирование систем, пайплайнов и DevOps-процессов. Требования: — Опыт 5+ лет в DevOps / IT Ops / DataOps с практическим опытом работы с инфраструктурой, связанной с данными. — Глубокие знания Linux (сеть, настройка ядра, оптимизация производительности). — Практический опыт работы с Airflow, ClickHouse, DBT, Spark и Project Nessie. — Солидные знания AWS (S3, EC2, IAM, сеть). — Владение скриптингом и автоматизацией (Python, Bash, Ansible). — Опыт работы с CI/CD пайплайнами (GitLab). — Опыт с мониторинговыми стеками (Prometheus, Grafana, ELK). — Знакомство с контейнеризацией и оркестрацией (Docker, Kubernetes). — Понимание концепций управления данными. — Сильные коммуникативные навыки и проактивный подход. Желательно: — Опыт работы с потоковой обработкой данных в реальном времени. — Знание инфраструктуры как кода (Terraform). — Знакомство с безопасностью в дата-инжиниринге (IAM, шифрование, контроль доступа). — Опыт работы с высокопроизводительными системами или в торговой среде.

Location: Pune, India Workplace: Hybrid Solvd Inc. is a rapidly growing AI-native consulting and technology services firm delivering enterprise transformation across cloud, data, software engineering, and artificial intelligence. We work with industry-leading organizations to design, build, and operationalize technology solutions that drive measurable business outcomes. Following the acquisition of Tooploox, a premier AI and product development company, Solvd now offers true end-to-end delivery—from strategic advisory and solution design to custom AI development and enterprise-scale implementation. Our capability centers combine deep technical expertise, proven delivery methodologies, and sector-specific knowledge to address complex business challenges quickly and effectively. Solvd is a rapidly growing AI-native consulting and technology services firm delivering enterprise transformation across cloud, data, software engineering, and artificial intelligence. We work with industry-leading organizations to design, build, and operationalize technology solutions that drive measurable business outcomes. We are looking for an experienced Snowflake Native App Architect to design and own the end-to-end architecture of a workforce intelligence platform being re-built as a native application on the Snowflake Marketplace. This is a technically broad, fast-paced engagement with a hard deliverable: a published, Snowflake-approved Marketplace application. You will work alongside a cross-functional team spanning data engineering, backend, DevOps, and QA — guiding all architectural decisions and leading the Snowflake Security Review process from start to finish.   WHAT YOU'LL DO Architecture & Design - Design the Application Package structure per Snowflake Native App Framework specifications, including permission declarations, egress declarations, and manifest. - Architect a cloud-agnostic strategy — Azure-first, with modular extensibility for AWS and GCP. - Design the end-to-end data flow from a desktop collector through to the customer's Snowflake environment, documenting all encryption points and access controls. - Evaluate and recommend migration tooling for the Talend → dbt/Snowflake transition. Platform & Compliance - Lead preparation for the Snowflake Security Review — package structure, data access patterns, network egress documentation. - Own provider account setup and the versioning/release architecture. - Produce the architecture diagram and compliance documentation package required for the Marketplace listing.   WHAT YOU BRING - 7+ years of experience in cloud or data architecture. - Hands-on experience with Snowflake, including the Native App Framework or Snowpark. - Proven track record delivering architecture documentation for platform reviews or compliance processes. - Experience publishing or contributing to a Snowflake Marketplace application. - Familiarity with dbt, Streamlit, or Snowflake Cortex. - Experience with Talend or similar ETL migration projects. - Strong written communication skills — this role produces documentation that goes to external reviewers. When you join Solvd, you'll… - Shape real-world AI-driven projects across key industries, working with clients from startup innovation to enterprise transformation. - Be part of a global team with equal opportunities for collaboration across continents and cultures. - Thrive in an inclusive environment that prioritizes continuous learning, innovation, and ethical AI standards. Ready to make an impact? If you're excited to build things that matter, champion responsible AI, and grow with some of the industry’s sharpest minds. Apply today and let’s innovate together. Solvd is an equal opportunity employer.