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Tech Stack
Tools & technologiesCloudETLPySparkSparkSQL
About the role
Key responsibilities & impact- Design, develop, and maintain scalable ETL/ELT data pipelines using Databricks and PySpark
- Build and optimize data lakehouse architectures (Bronze–Silver–Gold layers)
- Develop data ingestion frameworks from multiple sources (batch & real-time)
- Implement Delta Lake features including schema evolution, ACID transactions, and optimization
- Create and manage Databricks notebooks, workflows, and jobs for production pipelines
- Ensure data quality, governance, and lineage across pipelines
- Optimize Spark jobs for performance (partitioning, caching, tuning)
- Collaborate with cross-functional teams (Data Scientists, Analysts, Architects)
- Support production deployments, monitoring, and troubleshooting of data pipelines
- Contribute to migration of legacy data platforms to modern cloud-based architecture
Requirements
What you’ll need- 5–10 years of experience in Data Engineering
- Minimum 2–4 years of hands-on Databricks experience
- Proven experience building enterprise-scale data platforms and pipelines
- Strong experience in Databricks (Workflows, Notebooks, Jobs)
- PySpark / Spark SQL
- SQL & Data Warehousing concepts
- Hands-on experience with Delta Lake / Lakehouse architecture
- Data pipeline development & orchestration
Benefits
Comp & perks- Work on cutting-edge data & AI programs
- Opportunity to build large-scale data platforms
- Collaborative and innovation-driven culture
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
ETLELTDatabricksPySparkDelta LakeSpark SQLSQLData Warehousingdata pipeline developmentdata orchestration
Soft Skills
collaborationtroubleshootingdata governancedata quality
