Lead Data Engineer - AI/ML - R01551402

Primary Skills

• Athena, SNS, SQS, CloudWatch, Macie, Spark - Scala, Kinesis, CloudFormation, EMR, Open Search, DynamoDB, Amazon API Gateway, SCT, Redshift, DMS, Oozie

Job requirements

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• Location: Chicago, IL - Hybrid with 2-3 days on-site in a week

kill sets Must have : Python, SQL/PLSQL, AWS,Postgresql,S3, Glue Good to have : CDK, GitHub Job Description We are looking for an experienced AWS Lead Data Engineer to design, build, and manage robust, scalable, and high-performance data pipelines and data platforms on AWS. The ideal candidate will have a strong foundation in ETL fundamentals, data modeling, and modern data architecture, with hands-on expertise across a broad spectrum of AWS services including Athena, Glue, Step Functions, Lambda, S3, and Lake Formation.
Key Responsibilities:
• Design and implement scalable ETL/ELT pipelines using AWS Glue, Spark (PySpark), and Step Functions.
• Work with structured and semi-structured data using Athena, S3, and Lake Formation to enable efficient querying and access control.
• Develop and deploy serverless data processing solutions using AWS Lambda and integrate them into pipeline orchestration.
• Perform advanced SQL and PL/SQL development for data transformation, analysis, and performance tuning. Build data lakes and data warehouses using S3, Aurora, and Athena.
• Implement data governance, security, and access control strategies using AWS tools including Lake Formation, CloudFront, EBS/EFS, and IAM. Develop and maintain metadata, lineage, and data cataloging capabilities.
• Participate in data modeling exercises for both OLTP and OLAP environments.
• Work closely with data scientists, analysts, and business stakeholders to understand data requirements and deliver actionable insights. Monitor, debug, and optimize data pipelines for reliability and performance.

Required Skills & Experience:
• Strong experience with AWS data services: Glue, Athena, Step Functions, Lambda, Lake Formation, S3, EC2, Aurora, EBS/EFS, CloudFront.
• Proficient in PySpark, Python, SQL (basic and advanced), and PL/SQL.
• Solid understanding of ETL/ELT processes and data warehousing concepts.
• Familiarity with modern data platform fundamentals and distributed data processing.
• Experience in data modeling (conceptual, logical, physical) for analytical and operational use cases.
• Experience with orchestration and workflow management tools within AWS.
• Strong debugging and performance tuning skills across the data stack.

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