Data Engineer - Ii, Lmaq-de

Amazon Amazon · Big Tech · IN, TS, Hyderabad · Operations, IT, & Support Engineering

This role focuses on building and managing large-scale data integration and delivery services within Amazon's Last Mile Data Engineering Team. The primary responsibility is designing, developing, and maintaining data warehouse and data lake solutions, including ETL pipelines and data models, to support business decision-making, reporting, and machine learning model development. The team also drives innovation using Generative AI for features like natural language querying and workflow automation.

What you'd actually do

  1. designing, developing, troubleshooting, evaluating, deploying, and documenting data warehouse/data lake solutions, enabling stakeholders to manage the business and make effective decisions.
  2. build efficient, flexible, extensible, and scalable data models, ETL designs and data integration services.
  3. support and manage growth of these data solutions.
  4. working in one of the largest cloud-based data lakes.
  5. build complex ETL pipelines, near-real-time data ingestion systems, and reusable datasets that power analytics, business reviews, and machine learning model development.

Skills

Required

  • 3+ years of data engineering experience
  • 4+ years of SQL experience
  • Experience with data modeling, warehousing and building ETL pipelines
  • 2+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience
  • Experience building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets
  • Experience in Java, C++, Python, or a related language
  • Excellent written and verbal communication skills

Nice to have

  • Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
  • Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)

What the JD emphasized

  • massive scale of data
  • extremely large datasets
  • huge data sets

Other signals

  • large-scale data integration and delivery services
  • data warehouse/data lake solutions
  • ETL designs and data integration services
  • working in one of the largest cloud-based data lakes
  • complex ETL pipelines, near-real-time data ingestion systems, and reusable datasets that power analytics, business reviews, and machine learning model development
  • Generative AI-powered solutions for natural language querying, auto-insight generation, geocoding orchestration, and unified workflow automation