Data Engineer

We’re looking for a pragmatic and hands-on Full Stack Data Engineer to take end-to-end ownership of our data platform. We work with a founder’s mindset: act with urgency, make pragmatic trade-offs, and focus on the data problems that matter most to our users and the business. A key part of this role is data democratisation: bringing trusted data to everyone at Coople in a way that is easy to use, discoverable, and consistent. As a data-driven organisation, we make data available “everywhere” to enable data to deliver a tangible added value for our customers.

Our core stack includes Databricks (Data Lake) and Power BI (reporting). The next step is to evolve our data development environment and data stack into an AI-first setup: we want to use AI wherever it creates leverage (development, testing, documentation, data quality checks, incident response, and stakeholder enablement), and continuously optimize our stack and ways of working for faster, safer delivery.

To make data accessible, we also want to enable conversational access to data (a chat interface for metrics, definitions, and insights) so that non-technical stakeholders can reliably get answers fast, with clear context and trust signals. You will work closely with development teams and stakeholders in Warsaw (partly in person/hybrid) and you’ll often act as the “glue” between product engineering, business users, and clients in other locations too.

In this role you can expect a.o.

End-to-end data ownership

• Own the full data lifecycle: ingest data from source systems, build and maintain reliable pipelines into the Data Lake, and curate datasets that are trustworthy, traceable, and easy to use for analytics, product teams, and reporting. Deliver improvements through small, safe, frequent changes, and own their tests, data quality, observability, and behaviour in production - whether you or AI wrote the code.
• Deliver high-signal dashboards and reporting in Power BI, while partnering with stakeholders to refine requirements, standardise metric definitions, and improve discoverability and self-service through documentation, catalogs, and AI.

Data democratisation & decision enablement

• Make data easy to use for everyone at Coople by reducing friction to get answers and maintaining a clear single source of truth for key metrics, definitions, and ownership. Measure success by the user problems solved and better user decisions your work enables, not the number of pipelines or dashboards shipped.
• Enable a “data in every decision” culture by embedding insights into planning and operations, and supporting and help shaping a chat-based interface that lets stakeholders query data in natural language with reliable, contextualised answers.

AI-first data engineering

• Evolve the data stack and development environment toward AI-optimised workflows, including prompting patterns, automation, and review practices.
• Use AI to improve data quality, testing, lineage documentation, and operational runbooks while continuously improving speed, reliability, and maintainability of the platform.

Collaboration & operational excellence

• Partner closely with Warsaw-based development teams to ensure robust data contracts, event definitions, and integrations across systems. Other teams within Coople are based in London and Zürich (approx. 120 employees in total in all 3 locations)
• Maintain strong operational hygiene through monitoring, alerting, incident response, and continuous improvement of engineering standards and practices

Our stack & tools

• Data Lake: Databricks, AWS
• Reporting: Power BI
• Cloud: AWS
• Dev & Ops: GitHub (Actions/PRs), Datadog
• Productivity: Linear, Notion
• AI tooling: AI-assisted development with Cursor

Skills required

The skills we're looking for in the ideal candidate

• Around 5 years of experience with strong data engineering fundamentals: SQL, data modelling, ETL/ELT patterns, and operating data pipelines in production
• Databricks experience is a must (Spark/Delta, notebooks, jobs/workflows, Delta Lake concepts) and building/operating a Data Lake
• Power BI knowledge is a must (data modelling, measures, performance considerations, and stakeholder-facing reporting)
• Strong engineering standards: CI/CD, testing strategies, observability, performance tuning, and production readiness for data pipelines
• Experience owning data end-to-end: ingestion, transformations, data quality, and delivery to business-facing artifacts
• Experience enabling data democratisation (self-service, documentation, metric definitions, and usability for non-technical stakeholders) and interest in conversational data experiences (chat-based access to trusted metrics, definitions, and insights)
• AI-first development mindset: comfortable using AI tools to speed up delivery (e.g., code generation, review, test creation, documentation) and strong judgment on validation, correctness, and safety
• Product mindset and a bias for action: you take ownership, balance urgency with reliable delivery, and work closely with stakeholders to solve real problems rather than simply deliver requests
• Adaptability and a drive to improve: you actively seek feedback, absorb new context quickly, and change course when the evidence calls for it. You turn lessons from what worked and what didn’t into better data engineering practices for yourself and the team

Our interview process is designed to be transparent, and focused on mutual fit. You can expect a prescreening (conversation with our internal recruiter), technical interview (with the hiring team) and a final round with peers and/or our VPs.

Salary, benefits and hybrid model

• PLN 23'000 - 29'000 monthly base salary (employment contract, on request we could consider B2B too)
• On top of the base salary, we offer a yearly bonus (10% target bonus) and a company stock options plan for all employees (ESOP)
• A healthcare service package (Lux Med) and sports membership
• Hybrid working model: 2 days per week on-site in our office in the heart of Warsaw (next to metro Centrum / PKiN) is a must