Job Description:

Key Responsibilities

Design, develop, and maintain scalable data pipelines using Databricks and PySpark
Build robust ETL/ELT workflows for batch and near real‑time data processing
Write optimized and efficient SQL queries for data transformation, validation, and analysis
Work with large-scale distributed datasets using Spark-based processing frameworks
Perform data ingestion from multiple sources (APIs, files, databases) into data lake/lakehouse architecture
Ensure data quality, integrity, and consistency through validations and monitoring
Optimize pipeline performance and cost (partitioning, caching, query tuning, etc.)
Collaborate with cross-functional teams (Data Ops, Business, Analytics) to deliver data solutions
Troubleshoot pipeline failures and implement proactive monitoring and alerting
Maintain proper documentation for data models, transformations, and workflows

Required Skills & Experience

Strong hands-on experience in:
Databricks (Lakehouse platform, notebooks, jobs, clusters)
PySpark / Apache Spark
Advanced SQL (joins,



window functions, performance tuning)
Solid understanding of ETL concepts, data warehousing, and data modeling
Experience building scalable data pipelines for structured and semi-structured data
Familiarity with data lake / delta lake architecture
Good understanding of performance optimization techniques in Spark and SQL
Experience with version control (Git) and deployment practices
Strong problem-solving and analytical skills

Positive to Have

Experience with cloud platforms (AWS preferably)
Exposure to CI/CD pipelines (Jenkins)
Knowledge of Delta Lake, Unity Catalog, or similar governance tools
Understanding of data quality frameworks / monitoring tools
Experience working in Agile / Scrum teams

Location:

This position can be based in any of the following locations:

Chennai

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