Productboard is looking for an AI Data Engineer to join our Data Engineering team. You'll help build AI-native data engineering agents and a platform behind Productboard's analytics and business operations. You’ll help unlock AI-enabled analytics for fast and precise self-service data analytics. You are already an AI-native builder, experienced in modern data engineering, and you want to blend those fields together. You'll start by working alongside experienced engineers to implement AI-first analytics, streamline and optimize data pipelines, and progressively take ownership of your own area, including our internal AI platform.
What you will do
- Build Agentic Systems: Design and deploy multi-agent workflows that can reason, use tools, and provide proactive insights across internal domains.
- Engineer LLM-ready data. Shape the data model and build a semantic layer that serves as the clean context our AI agents depend on.
- Make agents reliable and fast to ship.
- Build evaluation frameworks and monitoring suites so agent outputs stay accurate, governed, and safe, and create reusable patterns that move prototypes to production quickly.
- Build and maintain pipelines. Develop the dbt models and Snowflake pipelines that power reporting across revenue, marketing, and product events.
- Keep data trustworthy. Investigate and resolve data quality issues, support stakeholders with ad-hoc requests, and adopt (then help evolve) the team's practices around testing, documentation, and monitoring.
- Contribute to how we work. Take part in code review, testing, deployment, and monitoring, and take ownership of part of it over time.
- Work directly with Analytics, BizOps, Product, and Engineering on what they need from the data.
About You
- 18+ months in data engineering, analytics engineering, or a similar role.
- Fluent with AI coding tools: You use tools like Claude Code, Cursor, or GitHub Copilot in your daily workflow, and know where they speed you up without lowering your standards.
- SQL and Python: Strong hands‑on experience building and maintaining Python and SQL pipelines.
- dbt and cloud warehousing: Hands‑on experience with dbt and production ETL pipelines, and familiarity with a modern cloud data platform such as Snowflake.
- Quality‑minded: You care about testing, maintainability, and reliability, and you're comfortable with Git, code review, and CI/CD fundamentals.
- Education: Bachelor's degree or higher in Computer Science, Engineering, Mathematics, or a related quantitative discipline.
- Collaborative: You work well in a fast‑moving, cross‑functional, multinational environment, and communicate clearly in English.
- Nice to Have: Experience building AI agents or LLM‑powered workflows with frameworks such as the Claude Agent SDK, LangGraph, or PydanticAI. Experience building an AI agent‑native semantic layer. Experience running large data warehouses, data lakes, or lakehouses in production. Familiarity with orchestration or ETL platforms such as Keboola, Airflow, or similar. Exposure to analytics platforms such as Looker, Power BI, or Tableau. Experience with AWS. Familiarity with Docker, Terraform IaaC, or other DevOps tooling. Experience in a fast‑moving AI‑native SaaS company.
Our Compensation
- Salary range: 1,000,000–1,680,000 CZK annually
Benefits include Stock options, MacBook + 34″ monitor, Work from home stipend, 5 weeks of vacation + 9 sick days, Flexible working hours and home office, Budget for online courses, books, and conferences, 2 weeks of fully paid parental leave, Fertility & Family‑Building Support, Mental Wellness Program, 1 Volunteer Day per year, Free snacks, drinks, and catered lunches, Free MultiSport card, On‑site bouldering wall, boxing bag, and workout mats, Team events, Free year‑round access to Prague Zoo, Relocation Opportunities.
- Location: Prague, Czechia
- Employment type: Full Time
- Workplace type: Hybrid