Posted:4 days ago|
Platform:
Work from Office
Full Time
Role & responsibilities Design, develop, and optimize scalable ETL pipelines using PySpark and AWS data services Work with structured and semi-structured data from various sources and formats (CSV, JSON, Parquet) Build reusable data transformations using Spark DataFrames, RDDs, and Spark SQL Implement data validation, quality checks, and ensure schema evolution across data sources Manage deployment and monitoring of Spark jobs using AWS EMR, Glue, Lambda, and CloudWatch Collaborate with product owners, architects, and data scientists to deliver robust data workflows Tune job performance, manage partitioning strategies, and reduce job latency/cost Contribute to version control, CI/CD processes, and production support Preferred candidate profile Bachelor's or Master's degree in Computer Science, Information Technology, or a related field. 5+ years of experience in PySpark, Spark SQL, RDDs, UDFs, and Spark optimization Strong experience in building ETL workflows for large-scale data processing Solid understanding of AWS cloud ecosystem, especially S3, EMR, Glue, Lambda, Athena Proficiency in Python, SQL, and shell scripting Experience with data lakes, partitioning strategies, and file formats (e.g., Parquet, ORC) Familiarity with Git, Jenkins, and automated testing frameworks (e.g., PyTest) Experience with Redshift, Snowflake, or other DW platforms Exposure to data governance, cataloging, or DQ frameworks Terraform or infrastructure-as-code experience Understanding of Spark internals, DAGs, and caching strategies
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