Data Engineer (Junior 1-3 Years)

The client (Client of Magnit) is looking for a Data Engineer (Junior 1-3 Years) for a hybrid role in Amsterdam, Netherlands. The role involves joining the Grundlig team as a Senior Data Engineer (note: the title says Junior but the description refers to Senior) and contributing to the design, development and operation of a foundational data product that consolidates and harmonises sales, payment, order and gift-card transactional data. The assignment includes building reliable batch, streaming and near-real-time data pipelines; developing reusable data models; improving data quality, monitoring and resilience; and supporting the secure and accessible delivery of trusted data to analysts and other consumers.

As a senior team member, the consultant will help guide architectural decisions and strengthen engineering practices, bringing practical Databricks and lakehouse expertise—including Apache Spark, Delta Lake, Unity Catalog and structured streaming—while working with and supporting Grundlig’s current GCP-based technology stack.

Day-to-day tasks include: designing, building, testing and operating batch, streaming and near-real-time data pipelines; ingesting and harmonising sales, payment, order and gift-card data from multiple source systems; developing transformations and production data models using SQL, Python, BigQuery and dbt; applying Databricks and Spark experience to scalable processing, streaming, governance and lakehouse-related design decisions; implementing validation, reconciliation, monitoring and automated data-quality controls; building resilient processing with error handling, recovery mechanisms and dead-letter queues; monitoring data freshness, pipeline reliability, performance and agreed service levels; protecting sensitive and personally identifiable information through secure data modelling and access controls; contributing to automated testing, CI/CD, code reviews and infrastructure automation; improving metadata, lineage, documentation and the experience of data consumers; mentoring colleagues and collaborating with source teams, analysts, product colleagues, privacy specialists and security stakeholders.

Required skills include senior-level experience designing, building and operating production data platforms and business-critical data products; strong hands-on Databricks experience including Apache Spark, Delta Lake, Unity Catalog, structured streaming, Databricks Workflows and lakehouse architecture; strong Python and advanced SQL skills; experience with distributed data processing and scalable batch and streaming pipelines; strong data-modelling skills; experience with data-quality validation, reconciliation, monitoring and alerting; knowledge of event-driven architecture, change data capture, schema evolution and resilient error-handling patterns; experience with Git, automated testing, CI/CD and modern software-engineering practices; knowledge of data security, IAM, privacy, PII handling, metadata and data lineage; and strong communication skills.

Desired skills include experience with GCP (BigQuery, Dataflow, Pub/Sub, Cloud Run, Cloud Storage), dbt, Apache Beam, Terraform, GitHub Actions, experience with high-volume transactional data, understanding of data-mesh principles, and experience in a product-oriented Agile or DevOps environment. The three most important skills are extensive hands-on Databricks expertise, senior-level experience with reliable scalable data pipelines, and strong technical leadership.