Databricks Data Engineer

Data Engineer with DatabricksWe are looking for a Data Engineer with strong Databricks experience to join a data & AI project focused on building a modern, scalable data platform supporting analytics, machine learning and AI use cases.

What you’ll doDesign, build and maintain scalable data pipelines using Databricks and Apache Spark.

Integrate data from multiple sources and build reliable, reusable data flows.

Develop and optimize data processing solutions for large volumes of data.

Work closely with Data Scientists, Analysts and business stakeholders to deliver data products supporting ML, BI and analytics.

Contribute to the development of a unified data platform and high-quality data layer.

Ensure data pipelines are reliable, scalable, performant and easy to maintain.

Optimize data processing and infrastructure with a focus on performance, scalability and cost efficiency.

Implement and maintain data quality, monitoring and data engineering best practices.

Support the development and evolution of modern lakehouse and cloud data architectures.

Your experience

Hands-on experience with Databricks is required.

Strong experience in Data Engineering and building production-grade data pipelines.

Strong SQL and PySpark / Apache Spark skills.

Experience working with large datasets and distributed data processing.

Good understanding of modern data platform, lakehouse and data architecture concepts.

Experience with cloud environments such as Azure, AWS or GCP.

Experience with Delta Lake and data orchestration tools is a strong advantage.

Experience working with different data sources, formats and integration patterns.

Ability to work closely with Data Scientists, Analysts and other technical stakeholders and understand their data requirements.

Strong problem-solving skills and a pragmatic approach to data engineering.

Relevant experienceYou should have hands-on experience in one or more of the following areas:

Data Platform & Lakehouse Engineering – building scalable platforms for analytics, reporting, ML and AI workloads.

Data Integration & Transformation – integrating structured and unstructured data from multiple source systems into reliable, reusable data pipelines.

Data Quality & Governance – implementing processes and frameworks for data quality, monitoring, lineage and governance.

Tech stack

Databricks, Apache Spark, PySpark, SQL, Delta Lake, Cloud (Azure / AWS / GCP)

Why join?

You’ll be part of a large-scale data & AI transformation, building the data foundations that power analytics, machine learning and AI use cases while working with modern Databricks and lakehouse architecture.