Senior Product Data Analyst

Описание вакансии

About Us

We're a fast-growing Series A startup backed by $34M in funding and driven by a team that moves fast, stays curious, and cares about high-quality execution.

About the Role

The Senior Product Data Analyst's primary responsibility is to design, build, and evolve data models, pipelines, and analytics tools that power our marketing data product. While client-facing analysts deliver custom solutions, your focus is on generalizing those solutions into scalable, reusable product components. You'll work with our internal BI platform, design data models for diverse marketing data sources, and ensure data quality across the product.

This role sits at the intersection of analytics and data engineering. You'll need strong SQL and dbt skills, a deep understanding of marketing data, and the ability to think in terms of product — turning one-off solutions into generalized, maintainable systems.

AI-first mindset is non-negotiable. We expect you to use AI tools (LLMs, copilots, code assistants) as your default approach to work — from writing SQL and dbt models to analyzing data, drafting documentation, and automating repetitive tasks. This isn't a nice-to-have; it's how we work. If you're already using AI daily and can't imagine going back, you'll fit right in.

We need someone we can fully rely on. You take a task, you own it end-to-end, you deliver on time with quality — no reminders needed, no corners cut. You're thorough, detail-oriented, and you check your own work before anyone else sees it. When something is unclear, you proactively drive clarity instead of waiting. This role has a clear path toward managing a team — we're looking for someone who's ready to grow into that.

Responsibilities

Design and maintain data models for marketing data across multiple data sources and complex, branching schemas.
Generalize custom client solutions into scalable product-level pipelines and tools.
Work with the internal BI platform — build and maintain dashboards, understand what makes a great marketing dashboard vs. a mediocre one.
Write, optimize, and troubleshoot SQL queries and dbt models for data transformations, reporting, and investigations.
Ensure data quality: conduct QA/UAT, validate data accuracy, consistency, and trustworthiness across pipelines and data sources.
Collaborate with product, engineering, and analytics teams to translate requirements into data solutions.
Proactively identify data quality risks, optimization opportunities, and areas for product improvement.
Use AI tools as your primary lever for productivity — writing code, analyzing data, generating documentation, automating workflows. Continuously explore new AI-powered approaches and share findings with the team.
Mentor junior analysts, review their work, and contribute to building team processes and standards. This role has a clear trajectory toward team leadership.
Use Git for version control and collaboration.

Must Have Qualifications

Mindset & Work Style (This is what matters most)

AI-first approach to work: You already use AI tools (ChatGPT, Claude, Copilot, or similar) daily as your default way of working — for SQL, analysis, documentation, and problem-solving. You understand their strengths and limitations, validate outputs, and continuously find new ways to leverage them.
Full ownership and accountability: You take a task and deliver a complete, quality result. No hand-holding, no reminders. You manage your own time, priorities, and commitments. When you say it's done, it's done right.
Results-driven and thorough: You focus on outcomes, not activity. You check your own work for accuracy and completeness before delivery. You don't cut corners.
Self-directed and proactive: You don't wait for instructions when something is unclear — you investigate, ask the right questions, and drive things forward.
Leadership potential: You're ready to grow into managing a team. You naturally mentor others, take responsibility beyond your own tasks, and think about how to improve processes and standards.

Technical Skills

3+ years of SQL experience, including complex queries (JOINs, CTEs, window functions).
2+ years of hands-on dbt experience: building and maintaining models, understanding layering approaches (staging → intermediate → marts), working with documentation and tests.
Experience designing data models — dimensional modeling, working with multiple data sources, and managing branching/complex schemas.
Strong understanding of marketing data: channels, funnels, attribution, key metrics (CAC, ROAS, CPA, etc.), and how data flows from ad platforms to analytics.
Product-oriented thinking: ability to see common patterns across custom solutions and propose generalized, reusable approaches.
English C1 level with excellent written and verbal communication.
Hands-on experience with QA/UAT processes.

Nice to Have

Python for data analysis and automation.
Experience with ClickHouse or other columnar databases.
Familiarity with marketing platform APIs and data (Google Ads, Facebook Ads, etc.).
Experience designing semantic/metric layers.
Understanding of CI/CD for data pipelines.
Knowledge of Marketing Mix Modeling (MMM) concepts.
Experience with GA4 / Google Analytics data streams.
Background in BI platforms (Looker, Power BI, Tableau) — for understanding best practices, not necessarily for daily use.