Data Engineer - II, LMAQ-DE
Confirmed on the employer's own hiring board on Oct 15, 2026. First seen by Alion on Sep 15, 2026. Amazon scores A on the Alion truth index.
Job Description:
Are you interested in being part of rapidly expanding Amazon initiative to redefine how we deliver packages to Amazon customers and take your skills and career to the next level? Join the Last Mile Data Engineering Team (LMDET) to raise the bar for our delivery channels and redefines Amazon’s global Last Mile delivery network.
Key job responsibilities
Last Mile Data Engineering Team is looking for a Data Engineer (DE) to play a significant role in building their large-scale, high-volume, high-performance data integration and delivery services. These data solutions would be primarily used in periodic reporting, and drive business decision making while dealing efficiently with the massive scale of data available through our Data Warehouse as well as our software systems. You will be responsible for designing, developing, troubleshooting, evaluating, deploying, and documenting data warehouse/data lake solutions, enabling stakeholders to manage the business and make effective decisions. You are expected to build efficient, flexible, extensible, and scalable data models, ETL designs and data integration services. You are required to support and manage growth of these data solutions. You must be a self-starter and be able to learn on the go. Excellent written and verbal communication skills are required as you will work very closely with diverse teams. As a Data Engineer, you will be working in one of the largest cloud-based data lakes. You should be skilled in the architecture of data warehouse solutions for the Enterprise using multiple platforms (EMR, RDBMS, Columnar, Cloud). You should have extensive experience in the design, creation, management, and business use of extremely large datasets. You should have excellent business and communication skills to be able to work with business owners to develop and define key business questions, and to build data sets that answer those questions. Above all you should be passionate about working with huge data sets and someone who loves to bring datasets together to answer business questions and drive change.
About the team
The Last Mile Data Engineering Team (LMDET) is a specialized data engineering organization focused on delivering seamless, secure, and governed access to Last Mile datasets at scale. The team designs and owns large-scale data lake and data warehouse infrastructures, including the Last Mile Data Repository (LMDR) and Campus data platforms, housing 10,000+ datasets across domains such as maps, geocoding, routing, and transportation. Leveraging AWS and industry-leading big data technologies, the team builds complex ETL pipelines, near-real-time data ingestion systems, and reusable datasets that power analytics, business reviews, and machine learning model development. LMDET also drives innovation through Generative AI-powered solutions for natural language querying, auto-insight generation, geocoding orchestration, and unified workflow automation, partnering closely with business, science, product, and technology teams to enable intelligent, data-driven decision-making across the Last Mile organization.
Basic qualifications
- 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 at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
Preferred qualifications
- 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)
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