About the role
Role & responsibilities
- Design, build and maintain scalable ETL/ELT pipelines using Databricks, PySpark, Spark SQL and Delta Lake.
- Implement Lakehouse solutions using Bronze/Silver/Gold architecture.
- Optimise Spark jobs, clusters and workloads for performance and cost.
- Build Databricks Workflows/Jobs and integrate with Airflow, ADF or equivalent orchestration tools.
- Implement data-quality checks, monitoring and observability.
- Support ML feature pipelines and MLflow-based workflows.
- Work with Unity Catalog for governance, access control and lineage.
- Develop robust data models with business and analytics teams.
- Support Git-based CI/CD and Databricks Repos.
- Troubleshoot production data-pipeline issues and perform root-cause analysis.
Preferred candidate profile
- 5 to 8 years of Data Engineering experience
- Minimum 2 to 3 years of hands-on Databricks experience
- PySpark
- Spark SQL
- Python
- Delta Lake
- Lakehouse / Medallion Architecture
- Azure Databricks / AWS / GCP
- Unity Catalog
- Databricks Workflows / Jobs
- Spark performance and cluster optimisation
- Strong SQL and data modelling
- Airflow / ADF / dbt
- Git and CI/CD
- Data governance, security and lineage
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