Data Analytics Engineer

• Pipeline Engineering: Build, deploy, and maintain robust transformation pipelines for high-volume data. You will participate in the full lifecycle: ingestion, transformation, testing (unit/integration), deployment, and monitoring.
• Optimization & Maintenance: Write highly efficient code and collaborate with the team to refactor legacy systems, helping to improve performance and reduce cloud compute costs (e.g., optimizing Athena/Snowflake/Redshift clustering or AWS Glue jobs).
• Software Engineering Best Practices: Adhere to and promote the team's technical standards by actively utilizing CI/CD workflows, containerization (Docker), and automated testing.
• Data Quality & Observability: Focus on infrastructure monitoring rather than just business dashboards. You will implement alerts and checks (e.g., dbt tests, Great Expectations) to catch data quality issues before they reach stakeholders.
• Collaboration & Growth: Work closely with Senior Engineers to plan architectures, participate actively in code reviews, and advocate for engineering rigor within the squad.

Your Profile

We are looking for a Data Analytics Engineer who approaches data with a software engineering mindset. You will join our Data Solutions squad to build and maintain production-grade data platforms.

This is not a Data Analyst role. While you will understand the business context, your primary focus is technical: building scalable workflows, writing clean and testable Python/SQL code, automating deployments, and supporting cloud infrastructure optimizations. You will ensure our pipelines remain reliable, cost-effective, and maintainable.

We are looking for a candidate who has solid analytics engineering experience or a strong background in backend development focused on data.

• 2+ years of experience specifically in Analytics Engineering or Data Engineering.
• Solid Python proficiency: You write modular, object-oriented code, utilize relevant libraries for testing, and understand exception handling and logging.
• Strong skills in SQL & dbt: You can build scalable data models (Jinja templating, macros, incremental strategies) and understand query execution plans.
• Software Engineering Fundamentals: Hands-on experience with Git flows, CI/CD pipelines (e.g., GitHub Actions, GitLab CI), and Containerization (Docker).
• AWS Cloud Native Experience: Experience building and maintaining data workflows using serverless architectures such AWS Lambda, StepFunctions, Glue, and Athena.
• Testing Mindset: Experience implementing Unit Tests and Integration Tests for data pipelines rather than relying solely on manual checks.
• Data Warehouse Ops: Solid understanding of warehousing architecture (Snowflake, Redshift, or BigQuery), including partitioning and clustering concepts.

Nice to Have

• Experience with Infrastructure as Code (Terraform) to manage cloud resources.
• Experience with orchestration tools like Airflow, Dagster, or Prefect.
• Knowledge of big data processing frameworks (Spark/PySpark).
• Experience with agentic coding CLI or IDE tools for more efficient planning, architecting and implementation of features.
• Familiarity with dashboarding tools (Streamlit, Preset, Tableau, etc.) - Note: This is helpful for debugging and monitoring, but not the core function of the role.
• B.S. or M.S. in Computer Science, Engineering, Mathematics or other relevant fields.

On top of a competitive package…

• We are open to flexible work models: we work in a hybrid mode and remotely from anywhere in the world up to 3 months per year.
• To encourage education and professional growth, we offer an individual yearly budget of 1.200€.
• You are entitled to 30 vacation days per year.
• We offer Urban Sports Club membership, company pension scheme.
• Regular team events and company events organised by our People team (Trust us, they know how to throw a party!).
• We equip you with the latest hardware and provide you with all the tools you need to thrive.

Salary Range (Annual On-Target Earnings)

60,000 - 75,000 euros

Recruiting Process

For technical roles (Data, Engineering, and Product), candidates are encouraged to upload any technical certifications referenced in their CV via the "Other Documents" section of the application form. Alternatively, certification documents may be submitted after the first technical interview. Any certifications listed on the CV must be provided and successfully validated before proceeding to the second technical interview stage.

• Step 1: Value-based interview (30 mins).
• Step 2: Deep-dive technical interview (1.5 hours) with the Data team.
• Step 3: Practical data-crunching challenge.
• Step 4: Team Meet & Greet - the final step to ensure we're a great fit for each other.