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Lead Data Engineer - PySpark / Spark

Lead Data Engineer - PySpark / Spark

SUPERSOURCING TECHNOLOGIES PRIVATE LIMITEDNoida
30+ days ago
Job description

About the Role :

We are looking for an experienced Lead Data Engineer with deep expertise in Big Data technologies, particularly within the Google Cloud Platform (GCP) ecosystem. The ideal candidate should have a strong command of PySpark / Spark, SQL, and Python, and a proven track record in building, optimizing, and managing large- scale data pipelines and cloud- native data platforms.

Key Responsibilities :

  • Lead the design and implementation of scalable ETL / ELT pipelines using Spark (batch and stream) and Python
  • Architect and optimize BigQuery solutions using advanced SQL, partitioning, clustering, and materialized views
  • Guide the team on GCP services : Dataproc, GCS, BigQuery, Cloud Composer, Cloud Functions, IAM, and Cloud Logging
  • Conduct code reviews and mentor team members on Spark optimization (caching, memory management, broadcast joins, skew handling)
  • Drive Airflow DAG development, configuration management, and orchestration workflows
  • Solve complex data engineering problems and contribute to architectural decisions for performance, scalability, and cost- efficiency
  • Ensure data quality, governance, and security best practices are enforced across all data platforms
  • Support team readiness through technical ramp- up and ongoing skill enhancement

Must- Have Skills :

  • Hands- on experience with PySpark / Spark core concepts, internal workings, transformations, and tuning
  • Strong knowledge of SQL and BigQuery (including window functions, CTEs, performance tuning, joins)
  • Proficiency in Python with strong problem- solving abilities
  • Deep experience with GCP components : BigQuery, Dataproc, GCS, and Cloud Composer
  • Understanding of Airflow, including XComs, variables, schema- based DAG creation, and branching
  • Exposure to Hive, partitioning (static / dynamic), and bucketed tables
  • Familiarity with data pipeline orchestration, monitoring, and failure handling
  • Solid grasp of data security (column- level, row- level, IAM roles)
  • (ref : hirist.tech)

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