SENIOR AWS DATA ENGINEER

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A Senior AWS Data Engineer designs, builds, and maintains scalable data pipelines (ETL/ELT) on AWS using services like S3, Glue, Redshift, Lambda, with Python/PySpark, SQL for complex transformations, orchestrating with tools like , ensuring data quality, security, governance, and performance optimization for analytics/BI, collaborating with stakeholders to translate needs into robust, cloud-native data solutions.

Key Responsibilities:

• Pipeline Development: Build, automate, and monitor scalable ETL/ELT data pipelines using Python, PySpark, Spark SQL, and AWS services (S3, Glue, Lambda, EMR, Redshift).
• Data Modeling & Architecture: Design and implement data models, data lakes, data warehouses, and lakehouse architectures for analytics.
• Orchestration: Develop and manage workflows using orchestrators like Airflow, EventBridge, or AWS Step Functions.
• Performance Optimization: Tune and optimize data pipelines and queries for speed, cost-efficiency, and reliability.
• Collaboration: Partner with data scientists, analysts, and business stakeholders to gather requirements and deliver data-driven solutions.
• Quality & Governance: Implement data quality checks, security (encryption, access controls), and governance best practices.
• Automation: Apply CI/CD, version control (Git), and testing to data workflows.
  
  

Core Skills:

• AWS: S3, Glue, EMR, Redshift, Lambda, Athena, Kinesis, SNS, SQS.
• Programming: Advanced Python (data manipulation, APIs), SQL (expert level).
• Big Data: Apache Spark, PySpark.
• Tools: Airflow, , , (often).
• Concepts: Data Warehousing, Data Lake, Data Modeling, Distributed Systems.