Senior Data Engineer Europe

We are seeking a Senior Data Engineer for an initial 6-month project, with the possibility of extension.

Together with the Zoolatech team, our partner, a leading construction management company, is at the forefront of developing a cloud-based software solution. The platform is instrumental in the successful completion of major projects including skyscrapers, hospitals, data centers, stadiums, and schools. It streamlines the management of finances, quality, teams, and risks, making these processes transparent, convenient, and regulation-compliant.

Our partner is not only an expert in construction management but also a significant technology innovator, actively developing web and mobile solutions. Zoolatech plays a crucial role in supporting their growth, and we are looking for result-oriented engineers who can maintain high development standards.

You’ll join the data engineering team behind the platform’s analytics and customer-facing insights. The team ingests data from multiple operational sources into a lakehouse architecture and turns it into trusted, well-governed datasets for data scientists, analysts and product teams.

Designing and maintaining scalable ingestion and transformation pipelines on Databricks, with jobs defined as code and deployed through CI/CD.

Owning end-to-end pipelines, including usage and business metrics pipelines and large-scale historical backfills across several data domains.

Consolidating and standardizing pipeline and job definitions into shared, reusable frameworks.

Supporting customer-health and usage analytics with reliable, well-modelled data.

Managing access, permissions and monitoring so that data is secure and issues are caught early.

Working with data science, platform and product teams to turn requirements into production-grade data products.

8+ years of hands-on data engineering experience.

Strong Python and SQL.

Airflow or a similar orchestration tool.

Hands-on experience with a major cloud platform, ideally AWS (e.g. S3, IAM, networking basics, cost and access management).

Strong software engineering practices:
Git Hub, CI/CD and code reviews.

Hands-on production experience in data modeling, backfills and data quality, beyond building basic pipelines.

Snowflake.

Daily use of AI coding tools (e.g. Claude, Cursor).

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