Data Engineer II, AWS Analytics Engineering - FDT

DataJobs; Seattle (WA); On-site; USD 132,000 - 179,000; Full time

Benefits offered by this job

RSUs

Health insurance

401(k) matching

Paid time off

Parental leave

Sign-on payments

Job summary

DataJobs in Seattle, WA seeks a Data Engineer II focused on AWS Analytics Engineering to build and operate the data platform supporting analytics across AWS services. You will own parts of the data architecture, data contracts, ingestion flows, and data models to deliver stable, high-performing pipelines with emphasis on quality, security, scalability and cost.

You will identify data quality issues, optimize data pipelines, and contribute to architecture decisions while mentoring peers and

Responsibilities

• Identify and resolve data quality issues in processing tools and uphold best practices for pipelines.
• Build and optimize logical data models and data pipelines addressing security, scalability, and cost.
• Write high-quality, secure, and maintainable code.
• Ensure code is understandable to engineers unfamiliar with the system.
• Drive data engineering best practices including code quality and operational excellence.
• Participate in on-call rotation and own operational health of data systems.

Job description

As a Data Engineer II focused on AWS Analytics Engineering, you will build and operate the data platform that supports analytics across AWS services. The role involves owning key parts of the data architecture, data contracts, ingestion flows, logical data models, and stable, high-performing data pipelines with an emphasis on quality, security, scalability, and cost.

Key Responsibilities

• Identify and resolve data quality issues in processing tools, contribute to pipeline improvements, and uphold best practices for the pipelines you design and maintain.
• Build and optimize logical data models and data pipelines for complex datasets, ensuring solutions are testable, maintainable, and efficient while addressing security, scalability, and cost.
• Make dataset-level technical trade-offs by balancing pragmatic short-term decisions with sustainable long-term approaches.
• Write high-quality code that is pragmatic, secure, maintainable, and flexible without unnecessary over-engineering.
• Ensure code is understandable to engineers unfamiliar with the system.
• Limit short-term workarounds and reduce incidental complexity across the data platform.
• Contribute to infrastructure decisions within the team’s data architecture.
• Manage resources effectively across system hardware, data storage, query optimization, and AWS infrastructure to deliver stable and performant solutions.
• Solve difficult problems such as designing data models that integrate multiple sources within the domain, and combining datasets to enable new analytical capabilities.
• Detect and proactively resolve issues that could cause data inconsistency or gaps in data quality.
• Break work into manageable tasks, deliver independently, and collaborate with peers on shared dependencies.
• Resolve discordant technical viewpoints and build consensus within the team.
• Mentor peers, participate in hiring, and contribute to team 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.
• Drive improvements through code reviews, design discussions, team planning, and operational reviews.
• Participate in an on-call rotation and take ownership of operational health for the data systems you support, including monitoring, alarming, runbooks, and incident resolution.

Required Qualifications

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

Technologies

• 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 including medical, dental, vision, prescription, Basic Life & AD&D, and options 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 and data stores including object storage, document or key-value stores, graph databases, and column-family databases.
• Experience with big data technologies such as Hadoop, Hive, Spark, and 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 and reporting/analytic tools and environments, data structures, and hands-on SQL coding experience.
• Experience operating highly available, distributed systems for data extraction, ingestion, and processing of large datasets, or experience with the software development lifecycle.

Role Details

• Location: Seattle, WA (onsite)
• Salary: USD 132,100 - 178,800 per year
• Minimum experience: 3 years