BUX
Hiring now
Hybrid · Amsterdam, North Holland, NetherlandsPosted 3 months ago
BUX
ApplyWe are looking for Junior Data Engineers to join our BUX team based in Amsterdam.
Our Data & AI team is a lean, international group of engineers dedicated to making BUX a truly data-driven fintech. We pride ourselves on being "platform-first", building tools that scale rather than just fixing one-off requests. We like automation, empowering our internal users, and serving our clients in the best way we can. We work closely with other internal and external stakeholders to ensure data is high-quality and actionable. Outside of the code, we’re a collaborative bunch who value open knowledge sharing, team lunches, and the occasional post-work drink in our hybrid office setup.
As a Junior Data Engineer at BUX, you will be a key builder of the analytics-ready data layer that powers our product decisions and business growth. You won't just write queries; you will build well-tested data ingestion scripts and transform raw data into high-quality, reliable data products. You will join the Data & AI team, collaborating closely with Analytics Engineers, Product stakeholders, and our Data Platform Engineers to leverage our scalable, self-service infrastructure safely. Your mandate focuses on execution and reliability: writing clean, testable code, owning the data quality of your pipelines, and documenting as you go. This is a role designed for growth, where curiosity, coachability, and an engineering-first mindset will serve as the foundation for your evolution into advanced analytics architecture or infrastructure-focused engineering tracks.
Build Reliable Ingestions: Build and maintain robust data ingestion scripts to pull data from APIs, servers, or files into raw/staging tables. You ensure correct handling of retries, incremental/idempotent loads, and graceful error management.
Model with dbt: Grow our analytics-ready data layer by building and maintaining clean staging and mart models with clear naming, documentation, and automated tests, focusing on simplicity and readability before optimisation.
Support Orchestration: Extend and troubleshoot our existing workflow orchestration frameworks (Airflow). You will add tasks to existing DAGs, fix failing runs, and grow into mastering dependency, scheduling, and retry behaviour.
Champion Data Quality: Own pipeline and model health by adding and monitoring data quality checks (freshness, volume anomalies, null/duplicate validations) and investigating data discrepancies flagged by different stakeholders.
Document & Accelerate: Write clean code, maintain useful READMEs, and leverage AI-assisted development tools to speed up drafting while taking full ownership of verifying correctness before pushing to production.
Grow your ownership: Build a solid track record with data modelling, testing, and pipeline reliability as a foundation for growing into orchestration design, platform topics, and broader engineering responsibilities over time.
You have 1-2 years of experience in data engineering, analytics engineering, or a closely related discipline, with a track record of collaborative problem-solving.
Solid fundamentals in Python and SQL, alongside comfort with version control (Git), writing basic automated tests, and interacting with HTTP APIs.
General understanding of foundational cloud architecture and infrastructure concepts.
Practical, hands-on experience (e.g. using dbt) for structuring data transformation layers, including staging/mart layering, sources, tests, and documentation.
A clear grasp of workflow orchestration concepts (such as Airflow), understanding DAGs, task dependencies, scheduling, retries, and the importance of idempotency.
Comfortable working with relational databases or data warehouses (Postgres, Snowflake, or similar) to write queries, design schemas, and handle data quality issues like nulls, duplicates, and freshness checks.
You are curious and comfortable asking for help. You welcome constructive feedback and actively contribute to the team's code quality.
You bring the critical eye needed to rigorously test and verify all logic, including AI-assisted code, before deployment.
Familiarity with Docker and how applications run on Kubernetes (GKE).
Basic exposure to Terraform and understanding how cloud environments are provisioned as code.
Initial exposure to high-throughput messaging (Kafka, Pub/Sub) or working with cloud object storage frameworks (GCS, S3).
A basic understanding of continuous integration pipelines (like GitHub Actions) and the concept of how code changes safely transition from a local machine to a production environment.