Data Engineer

Build reliable data pipelines and models on enterprise-scale datasets — and help shape internal AI developer tools through real daily use.

🧠 Role overview

At CodeStrap, we build Larry AI — internal AI coding tooling used by our delivery teams on complex, high-stakes work for Fortune 500 companies.

We are looking for a Data Engineer to join our Larry AI Delivery team in Kraków. You will work on brownfield data projects where architecture decisions still matter: ingesting structured and unstructured sources, modeling data properly in the warehouse, and connecting analytics platforms so teams can operate on reliable data.

This is a hands-on delivery role. You will spend most of your time building pipelines and models, but you will also use Larry AI in your daily work and feed back what works and what does not — helping the platform team improve the tools you rely on.

You will collaborate with an experienced data engineering lead and work closely with a US-based team, with regular syncs in the late afternoon (CET).

✅ Requirements

💼 Experience

• 4–5 years of commercial experience in Data Engineering
• Confidence owning pipeline and modeling work from requirements through production
• Practical experience on real business datasets, not toy projects only

🏗️ Technical foundation

• Strong SQL and Python — you understand why your code works, not just that it runs
• Hands-on experience with Snowflake and dbt (or equivalent warehouse + transformation tooling such as BigQuery, Databricks, or Postgres-based stacks)
• Solid data warehousing and modeling fundamentals — schemas that scale and stay maintainable
• Ability to integrate structured and unstructured sources into a coherent warehouse layer

🤝 Communication and collaboration

• Excellent English (written and spoken) for daily work with a US-based team
• Clear, direct communication about trade-offs, blockers, and modeling decisions
• Comfort joining regular late-afternoon syncs (typically around 4–5 PM CET)

🛠️ Ownership

• Proactive about spotting bottlenecks in pipelines and models
• Willingness to push back on suboptimal modeling decisions with reasoning, not politics
• Ability to pick up new tools quickly when the project requires it

🌍 What you can expect

• Real impact: Work on live data problems where your modeling and pipeline choices shape outcomes
• Tooling feedback loop: Use Larry AI daily and influence how internal AI developer tools improve
• Learning: Exposure to Palantir Foundry and other enterprise data platforms on active projects

🧩 Responsibilities

1. Data ingestion and integration: Build pipelines to ingest structured and unstructured data — including complex documents such as PDFs — into Snowflake.
2. Modeling: Design and implement scalable, efficient data models with dbt and warehouse best practices.
3. Platform integration: Connect Snowflake data with Palantir Foundry and related systems to support operational analytics.
4. AI tooling feedback: Use Larry AI in delivery work and provide concrete feedback to help refine internal coding assistants.
5. Quality and ownership: Identify pipeline bottlenecks, improve data quality, and advocate for sound modeling decisions.

🧪 Recruitment process

1. 1h technical conversation focused on SQL, Python, and data modeling
2. 1h meeting with the team lead and delivery stakeholders
3. Decision and offer

🏢 Perks in the office

• ☕ Free coffee
• 🚲 Bike parking
• 🚿 Shower
• 🥤 Free beverages
• 🍿 Free snacks
• 🏙️ Modern office
• 👕 No dress code
• 🎮 Playroom

🎁 Benefits

• 💻 MacBook Pro provided
• 📚 Training budget
• 🕒 Flexible working hours with outcome-based culture (US sync required)