About Gapstars
At Gapstars, we partner with some of Europe's most ambitious companies, from disruptive tech startups to established Benelux accounting firms, helping them build high-performing teams in engineering and finance.

Headquartered in the Netherlands, with talent hubs in Sri Lanka and Portugal, we are home to 300+ professionals who thrive on solving real-world challenges with modern solutions. Our teams work across domains, from networking, marketplaces, SaaS, and AI to embedded finance and accounting, delivering scalable solutions that drive meaningful outcomes.
 

About the role
As a Senior Data Engineer, you will work across Google Cloud Platform (GCP) and Databricks, building and evolving scalable data pipelines, data products, and analytics-ready datasets.

The current environment is approximately 60–70% GCP-focused, so strong hands-on GCP experience is mandatory. Initially, your work will primarily focus on the existing GCP platform, with increasing ownership of Databricks pipelines, integrations, and lakehouse capabilities as the platform evolves.

You will also work closely with Data Scientists and Analytics teams to deliver trusted, governed, AI-ready and reporting-ready datasets.

Key Responsibilities

1) GCP Data Engineering

• Design and maintain scalable data architectures and pipelines on GCP.

• Build and optimize solutions using BigQuery, Dataflow, Cloud Run, Composer, and GCS.

• Develop reliable pipelines supporting analytics, reporting, and machine learning workloads.

• Translate business requirements into scalable technical solutions.

• Maintain high standards around performance, reliability, security, and cost.

2) Databricks Engineering

• Own and develop Databricks pipelines and integrations.

• Work with Delta Lake, Unity Catalog, Workflows, MLflow, and Spark.

• Support the gradual expansion of Databricks within the wider data platform.

• Ensure GCP and Databricks workloads follow consistent engineering, governance, and security standards.

• Help shape future lakehouse architecture and integration patterns.

3) Data Transformation & Semantic Layer

• Develop transformation workflows using SQL, Python, and dbt.

• Build and maintain reusable semantic layers and data models.

• Deliver datasets that are ready for analytics, reporting, AI, and ML use cases.

• Establish standards for testing, documentation, versioning, and deployment.

4) Data Quality, Governance & Reliability

• Implement data quality controls and automated testing.

• Ensure data accuracy, governance, security, and accessibility.

• Monitor pipeline health, freshness, performance, and operational stability.

• Troubleshoot incidents and drive continuous improvement.

• Support data lineage, access controls, and lifecycle management.

5) AI / ML Enablement

• Work closely with Data Scientists and Analytics teams.

• Understand ML workflows and the data requirements behind them.

• Build trusted and reusable datasets for ML and AI use cases.

• Support ML pipelines and integrations, including the use of MLflow where applicable.

• Help create AI-ready datasets through the semantic layer.

6) Platform Ownership & Engineering Standards

• Contribute to infrastructure and deployment standards using Terraform, Docker, CI/CD, and Git.

• Promote engineering best practices around testing, documentation, code reviews, and operational ownership.

• Provide technical guidance and mentor other engineers.

• Document architectures, data flows, integrations, and operational processes.

7) Collaboration & Stakeholder Management

• Work with Product Owners, Data Scientists, Analysts, Engineers, and business stakeholders.

• Translate business needs into scalable technical solutions.

• Communicate technical decisions, trade-offs, risks, and progress clearly.

• Take ownership of solutions from design through production.