We are looking for a Data Pipeline Engineer with strong expertise in Databricks, AWS Glue, Python, SQL, and MongoDB to design, develop, and optimize scalable data pipelines. The ideal candidate should have experience building cloud-native ETL solutions, transforming large datasets, and delivering high-quality data for analytics and business applications.
Key Responsibilities:
● Develop and maintain scalable ETL/data pipelines using Databricks and AWS Glue.
● Build data ingestion and transformation workflows for structured and semi-structured data.
● Write efficient PySpark, Python, and SQL code for large-scale data processing.
● Integrate data from multiple sources, including MongoDB and relational databases.
● Implement data quality, validation, and reconciliation checks.
● Optimize pipeline performance, reliability, and scalability.
● Collaborate with architects, analysts, and application teams to deliver data solutions.
● Support production deployments, monitoring, and troubleshooting.
Required Skills:
● 5+ years of experience in Data Engineering or ETL development.
● Strong hands-on experience with Databricks and PySpark.
● Experience with AWS Glue and AWS data services.
● Proficiency in Python and SQL.
● Experience working with MongoDB.
● Knowledge of ETL, data modeling, and data warehousing concepts.
● Familiarity with Delta Lake/Lakehouse architecture is preferred.
● Experience with Git and Agile development practices.
Good to Have:
● Delta Lake.
● Apache Spark optimization.
● AWS S3.
● CI/CD pipelines.
● Healthcare or Benefits domain experience.
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