Data Engineer II, AWS Analytics Engineering - FDT

Amazon Development Center U.S., Inc. builds the AWS Analytics Engineering platform that powers analytics across AWS services. As a Data Engineer II, you will help design, build, and operate parts of the data platform, with ownership spanning data architecture, data contracts, ingestion flows, logical data models, and high-performing data pipelines. This role emphasizes data quality, security, scalability, and cost, backed by an environment that supports operational excellence and continuous improvement.

Location: Seattle, WA (onsite)

Compensation: USD 132,100 - 178,800 per yearly

Experience: Minimum 3 years

What you’ll do

• Identify and resolve data quality issues in processing tools, then contribute improvements to pipeline design and best practices.
• Build and optimize logical data models and data pipelines for complex datasets, keeping solutions testable, maintainable, and efficient while addressing security, scalability, and cost.
• Make dataset-level technical trade-offs that balance short-term pragmatism with long-term sustainability.
• Write high-quality, pragmatic and secure code that remains maintainable and flexible without over-engineering.
• Ensure the work is understandable to engineers unfamiliar with the system and limit short-term workarounds to reduce incidental complexity.
• Contribute to infrastructure decisions within the team’s data architecture and efficiently manage resources such as hardware, storage, query performance, and AWS infrastructure.
• Solve difficult data modeling and integration problems, including combining multiple sources and enabling new analytical capabilities through dataset integration.
• Proactively detect and resolve issues that could lead to data inconsistency or quality gaps.
• Break down projects into manageable tasks delivered independently, collaborating with peers on shared dependencies and aligning on approaches through consensus-building.
• Mentor peers, participate in hiring, and contribute to knowledge sharing.
• Drive data engineering best practices including code quality, data certification, dependency management, and operational excellence.
• Establish SLAs, automate manual processes, and improve self-service access to data.
• Improve the platform through code reviews, design discussions, team planning, and operational reviews.
• Participate in an on-call rotation and take ownership of operational health for systems you support by contributing to monitoring, alarming, runbooks, and incident resolution.

Minimum qualifications

• 5+ years of data engineering experience
• 3+ years developing and operating large-scale data structures for BI analytics using ETL/ELT
• 3+ years developing and operating large-scale BI analytics data structures using SQL
• 5+ years analyzing and interpreting data with Redshift, Oracle, NoSQL, etc.
• 3+ years developing and operating large-scale BI analytics data structures using OLAP technologies and data modeling
• Experience communicating with users, other technical teams, and management to collect requirements and describe data modeling decisions and data engineering strategy

Technologies you may work with

• SQL, ETL, ELT
• AWS EMR, AWS Glue, Amazon Redshift, Lake Formation
• AI/ML, LLMs, Agentic Frameworks, autonomous agents, multi-agent orchestration, tool integration
• Kinesis, FireHose, Lambda, IAM roles and permissions
• Hadoop, Hive, Spark
• Oracle, OLAP

Benefits

• Sign-on payments
• Restricted stock units (RSUs)
• Health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
• 401(k) matching
• Paid time off
• Parental leave

Preferred qualifications

• Experience with AWS technologies such as 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)
• Experience with big data technologies including Hadoop, Hive, Spark, EMR
• Experience working with Data & AI related technologies, including AI/ML, GenAI, analytics, databases, and/or storage
• 4+ years of data warehouse technical architectures, data modeling, infrastructure components, ETL/ELT and reporting/analytic tools and environments, data structures, and hands-on SQL coding
• Experience operating highly available, distributed systems for data extraction, ingestion, and processing of large data sets, or experience with the software development lifecycle