Data Engineer-Data Platforms-AWS

About the Role

We are seeking an experienced AWS Data Engineer to design, build, and support scalable, secure, and high-performance data platforms on AWS. The role requires strong hands-on experience with AWS services, distributed data processing frameworks, and SQL-driven analytics. The ideal candidate will work closely with architects, application teams, and business stakeholders to deliver reliable enterprise-grade data solutions.

Key Responsibilities

• Design, develop, and maintain data pipelines and data platforms on AWS
• Build and operate serverless and containerized workloads using AWS Lambda, EC2, and EKS
• Develop batch and large-scale data processing solutions using Apache Spark
• Implement data ingestion, transformation, and analytics using AWS Glue and Athena
• Write complex and high-performance SQL queries for analytics, reporting, and data validation
• Manage and optimize data storage solutions using AWS S3 and RDS
• Design and manage AWS infrastructure using Infrastructure as Code (IaC) with CloudFormation
• Apply AWS security best practices across IAM, VPC networking, data encryption, and access controls
• Develop clean, scalable, and maintainable code using Python and/or Scala
• Monitor, troubleshoot, and optimize performance, cost, and reliability of data workloads
• Collaborate with cross-functional teams to support analytics, reporting, and regulatory use cases

Requirements & Tech Stack

• Strong hands-on experience with AWS services, including:
• S3, Lambda, EC2, EKS
• AWS Glue, Athena
• RDS
• VPC, IAM
• CloudFormation
• Strong SQL skills, including:
• Writing complex queries, joins, aggregations, and user defined functions
• Performance tuning and query optimization
• Good knowledge of AWS Security, including IAM policies, VPC security, encryption, and least-privilege access
• Hands-on programming experience with Python and/or Scala
• Strong experience with Apache Spark for large-scale data processing
• Good communication skills to explain technical concepts to non-technical stakeholders
• Solid understanding of data engineering concepts such as ETL/ELT, data modeling, and distributed systems
• Exposure to AWS EMR, Glue Catalog, and Kinesis to design, build, and operate batch and real-time data pipelines
• Exposure to developing batch and real-time data pipelines for Data Warehouse and Datalake, utilizing AWS Kinesis and Managed Streaming for Apache Kafka
• Experience working with AWS RedShift, Aurora, and DynamoDB to create data layers and migrate data using AWS DMS
• Exposure to utilizing open source technologies like Apache Airflow and dbt, Spark / Python or Spark / Scala on AWS Platform to support data engineering solutions
• Experience scheduling and managing data services on the AWS Platform
• Experience working with AWS Glue Databrew
• Exposure to using Lambda functions with Python
• Experience working with RedShift Spectrum

Qualifications

• Master's Degree (Preferred Education)
• Strong analytical and problem-solving skills
• Ability to work independently and in distributed teams

Preferred Experience

• Experience with DBT for data transformations
• Hands-on exposure to Snowflake
• Experience working with Databricks
• Familiarity with modern data lake / Lakehouse architectures
• Experience working in enterprise or regulated environments