Data Engineer II, AWS Analytics Engineering - FDT

Join a team building and operating the AWS Analytics Engineering data platform that powers analytics across AWS services. As a Data Engineer II, you will take ownership of major parts of the data architecture, including data contracts, ingestion flows, logical data models, and reliable, high-performing data pipelines. The focus is clear: improve data quality, strengthen security, scale to meet demand, and control cost through practical engineering decisions.

What you’ll do

• Identify and resolve data quality issues in processing tools, drive pipeline best practices, and contribute improvements through innovation.
• Build and optimize logical data models and data pipelines for complex datasets, ensuring solutions are testable, maintainable, and efficient while accounting for security, scalability, and cost.
• Make dataset-level technical trade-offs that balance short-term pragmatism with long-term sustainability.
• Write high-quality code that is pragmatic, secure, maintainable, and flexible, without unnecessary over-engineering.
• Ensure code can be understood by engineers who are unfamiliar with the system and reduce reliance on short-term workarounds and incidental complexity.
• Contribute to infrastructure decisions within the team’s data architecture and manage resources such as hardware, storage, query optimization, and AWS infrastructure.
• Solve difficult engineering problems, including integrating multiple sources through data models and combining datasets to unlock new analytical capabilities.
• Proactively detect and resolve risks that lead to data inconsistency or gaps in data quality.
• Break work into manageable tasks, deliver independently, and collaborate effectively on shared dependencies.
• Resolve differing viewpoints and build consensus with peers.
• Support team execution through mentoring, participation in hiring, and knowledge sharing.
• Drive data engineering best practices across code quality, data certification, dependency management, and operational excellence.
• Establish SLAs, automate manual processes, and improve self-service access to data.
• Improve systems through code reviews, design discussions, team planning, and operational reviews.
• Participate in on-call rotation and take ownership of operational health for owned data systems, including monitoring, alarming, runbooks, and incident resolution.

Key skills and experience

• 5+ years of data engineering experience.
• 3+ years developing and operating large-scale data structures for business intelligence analytics using ETL/ELT.
• 3+ years developing and operating large-scale business intelligence data structures using SQL.
• 5+ years analyzing and interpreting data with Redshift, Oracle, and NoSQL experience.
• 3+ years developing and operating large-scale business intelligence data structures using OLAP technologies and data modeling experience.
• Experience communicating with users, other technical teams, and management to collect requirements and explain 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, 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 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).
• Experience with big data technologies such as Hadoop, Hive, Spark, EMR.
• Experience working with Data & AI related technologies including AI/ML, GenAI, Analytics, Database, and/or Storage.
• 4+ years of data warehouse technical architectures, data modeling, infrastructure components, ETL/ELT, 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.

Location: Seattle, WA (onsite)
Compensation: USD 132,100 - 178,800 per yearly