AWS Data Engineer

Industry & Sector

Primary Title:

• Design and implement scalable ETL/ELT data pipelines on AWS for batch and streaming workloads using S3, Redshift, Glue, and Spark.
• Develop PySpark jobs and Python services to transform, validate, and aggregate large datasets, ensuring data quality and lineage.
• Build and maintain data warehouse schemas, partitioning strategies, and performance optimizations for Redshift and data lake queries.
• Automate deployments and infrastructure using Terraform and CI/CD pipelines; manage IaC for reproducible environments.
• Instrument observability—logging, metrics, and alerts—for ETL workflows; troubleshoot production incidents and optimize throughput and cost.
• Collaborate with data scientists, analysts, and product teams to translate business requirements into reliable data models and APIs.
  

Skills & Qualifications

Must-Have

• AWS, Python / PySpark : Min 4+ yrs of experience
• Experience in RDD and Map functions in PySpark
• AWS services – Glue, Lambda, Redshift, Step functions, Cloud formation / CFT
• Good to have
• Lake formation / LFT
  

Preferred

• Apache Airflow
• AWS Lambda
• AWS Certified DataAnalytics-Specialty
  

• Hands-on ownership of end-to-end data platform projects and opportunity to influence architecture decisions.
• Collaborative, engineering-first culture with focus on automation, observability, and continuous improvement.
• On-site role in India offering close collaboration with cross-functional teams and rapid career growth.
  

Location & Workplace: