Databricks Data Engineer

Data Engineer with Databricks

We 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 do

• Design, 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 experience

You 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.