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.