Data Engineer
Posted Updated
Summary
A hands-on Data Engineer designing, building, and optimizing scalable batch and streaming data pipelines on the Databricks Lakehouse platform using PySpark, Delta Lake, and Unity Catalog, while collaborating with analysts, data scientists, and business teams to deliver reliable, analytics-ready data assets.
RoleOverview
Weare looking for a hands-on Data Engineer to design, build, and optimizescalable data pipelines on the Databricks Lakehouse platform. The role involvescollaborating with analysts, data scientists, and business teams to deliverreliable, analytics-ready data assets.
KeyResponsibilities
- Build and maintain batch and streaming data pipelines using PySpark and Databricks .
- Implement medallion architecture (Bronze, Silver, and Gold layers).
- Optimize Spark jobs for performance and cost efficiency.
- Ensure data quality, lineage, and governance using Unity Catalog and automated validation.
- Integrate data from APIs, databases, files, and event streams.
- Implement CI/CD, version control, and workflow orchestration.
- Monitor, troubleshoot, and document data pipelines and data models.
- Collaborate with stakeholders to translate business requirements into technical solutions.
RequiredQualifications
- 3-5 years of hands-on data engineering experience.
- Strong PySpark skills, including DataFrames, Spark SQL, UDFs, and performance tuning .
- Proven experience building production pipelines on Databricks.
- Working knowledge of Delta Lake, including ACID transactions, time travel, merge/upsert, and schema evolution .
- Advanced SQL and strong Python programming skills .
- Experience with Azure, AWS, or GCP cloud platforms.
- Understanding of data modelling, Lakehouse architecture, and ETL/ELT patterns.
- Familiarity with Git and CI/CD practices.
PreferredQualifications
- Databricks Data Engineer Associate or Professional certification.
- Experience with Unity Catalog, Auto Loader, and Structured Streaming.
- Familiarity with Airflow, Azure Data Factory, or Databricks Workflows.
- Knowledge of Terraform and testing frameworks such as pytest.
- Exposure to Kafka, Event Hubs, dbt, or MLOps workflows.
- Experience working in regulated or enterprise environments.
KeySkills
- PySpark and Python
- Databricks and Delta Lake
- Advanced SQL
- Data Pipeline Development
- Medallion Architecture
- Cloud Data Engineering
- Data Quality and Governance
- CI/CD and Git
- Spark Performance Tuning
DesiredAttributes
- Strong problem-solving and debugging skills.
EA Number: 11C4879