Lead Data Engineer (Databricks, PySpark & GCP)
Egen (company site, opens in a new tab)10–16 yrsHyderabadOn-siteDirect career pageEst. 25–40 LPA
Posted on 8 Sept
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Job description
Structured overview of role & requirements
About This Role
- Own the end-to-end design, architecture, development, testing, and maintenance of scalable ETL/ELT data pipelines using Python, PySpark, Databricks, and GCP/Azure.
- Directly engage with customers to gather requirements, perform solution discovery and architect enterprise data solutions leveraging GCP services including Dataflow, BigQuery, Cloud Composer, and others.
- Ensure data quality, consistency, pipeline optimization, and post-delivery support while collaborating with cross-functional teams including data scientists and analysts.
Minimum Requirements
- 10 to 16 years of relevant IT experience in backend or data engineering roles.
- Strong hands-on experience with Python, PySpark, Databricks notebooks and workflows, and Spark SQL.
- Proven expertise working with Google Cloud Platform services such as Dataflow, BigQuery, Cloud Functions, Cloud Composer, IAM, Cloud Run.
- Bachelor's degree in Computer Science or related field or equivalent experience.
Ideal Candidate Profile
- Experienced in architecting data solutions using Medallion Architecture (Bronze, Silver, Gold layers) and Delta Lake implementation on Databricks.
- Skilled in tuning Spark jobs, optimizing cluster configurations, and managing large-scale data processing performance.
- Familiar with cross-cloud tools, including Azure Data Factory, Azure Databricks, and CI/CD pipelines using GitHub, showcasing a versatile cloud data engineering background.