Senior AWS Data Engineer

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Job Purpose

Senior Data Engineer

AWS cloud technologies

data pipelines

data lake solutions

AWS services, Python/PySpark, and SQL

Key Responsibilities

• Design, build, and maintain data pipelines for large-scale data ingestion, transformation, and orchestration.
• Work with AWS cloud technologies such as S3, Lambda, Glue, Glue Catalog, Athena, DMS, DynamoDB, EC2, ECS, and ECR.
• Develop efficient ETL/ELT processes to integrate structured and unstructured data into data lakes and data warehouses (Snowflake).
• Implement automation and infrastructure-as-code using AWS CloudFormation.
• Collaborate with cross-functional teams to understand business requirements and translate them into scalable data engineering solutions.
• Ensure data quality, performance, and reliability of solutions.
• Write optimized SQL and PL/SQL queries for data analysis, reporting, and transformation.
• Leverage PySpark and Python for data processing, analytics, and pipeline orchestration.
• Continuously evaluate and adopt new cloud-native data engineering tools.
  

Essential Skills

• 6–7 years of IT experience, with at least 3+ years in data engineering / data architecture.
• Strong hands-on expertise with AWS services: Lambda, S3, Glue, Glue Catalog, Athena, DMS, DynamoDB, EC2, ECS, ECR, CloudFormation.
• Proficiency in Python and PySpark for data processing and transformation.
• Solid understanding of data lakes, ingestion, extraction, transformation, and orchestration.
• Good knowledge of SQL and PL/SQL concepts.
• Proven experience with Snowflake integration and large-scale data processing.
• Strong problem-solving, analytical, and multitasking skills.
• Self-motivated, proactive, and quick learner with ability to work independently and in teams.