Aws Data Engineer

Job Description

Mandatory keyskills : Athena, Step Functions, Spark - Pyspark, ETL Fundamentals, SQL (Basic + Advanced), Glue, Python, Lambda, Data Warehousing, EBS /EFS, AWS EC2, Lake Formation, Aurora, S3, Modern Data Platform Fundamentals, PLSQL, Cloud front We are looking for an experienced AWS 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.