Senior Data Engineer (AWS Analytics Engineering)

Quick Facts

• Role: Senior Data Engineer (AWS Analytics Engineering)
• Scope: Own 3–5 data domains end-to-end, operate hundreds of pipelines, and drive data architecture improvements for AWS leadership decision-making

Description

You will design, build, and maintain the data platform that powers analytics across many AWS services at massive scale. The role includes engineering ingestion and transformation pipelines, analytical data models, and secure infrastructure, while delivering architectural improvements and reliability. You will use deep SQL/ETL expertise with AWS data services and also work with AI/ML (including LLMs) and agentic frameworks to solve ambiguous cross-domain data problems.

Responsibilities

• Identify limitations and improvement opportunities in data processing tools; define guidelines and ensure best practices in pipelines
• Define and own team-level data architecture, balancing security, scalability, and cost with sound technical trade-offs
• Produce exemplary, maintainable, secure, extensible code; simplify and optimize pipelines and remove bottlenecks
• Define and own team-level infrastructure architecture and automation practices
• Solve complex ambiguous problems (e.g., cross-domain data models and dataset unification)
• Split project work into parallel tasks and deliver projects with cross-team dependencies
• Influence related teams’ data architecture and software design; mentor and build consensus
• Drive data engineering best practices: Data Discovery, Naming Conventions, Operational Excellence, Data Security
• Proactively fix data architecture deficiencies and propose larger cross-team initiatives
• Participate in on-call rotation; ensure operational health via monitoring, alarming, runbooks, and SLA tracking; drive continuous reliability improvements

Requirements

• 7+ years of data engineering experience
• Experience with at least one modern scripting or programming language: Python, Java, Scala, or NodeJS
• Experience mentoring team members on best practices
• Experience building/operating highly available distributed data extraction, ingestion, and processing systems at large scale
• Experience building data products incrementally and integrating/managing data from multiple sources
• Experience communicating with users, technical teams, and management to gather requirements and explain data modeling and engineering strategy
• Experience providing technical leadership and mentoring engineers
• Experience with MPP databases such as Amazon Redshift
• Bachelor’s degree in computer science, engineering, analytics, mathematics, statistics, IT, or equivalent
• 5+ years of data warehouse technical architecture, data modeling, infrastructure components, ETL/ELT, reporting/analytic tools and environments, plus hands-on SQL coding
• Ability to participate in on-call rotation and own operational health (monitoring, alarming, runbooks, SLAs)

Preferred Qualifications

• Experience with Hadoop, Hive, Spark, EMR
• Experience operating large data warehouses
• Experience with Data & AI technologies (AI/ML, GenAI, analytics, database, and/or storage)
• Experience delivering results for large cross-functional initiatives and communicating to senior leadership
• Experience with enterprise-scale infrastructure or cloud development programs/projects in related industries
• Experience with highly available distributed data extraction/ingestion/processing systems and/or familiarity with the software development lifecycle

Benefits

• Sign-on payments and restricted stock units (RSUs)
• Comprehensive health insurance (medical, dental, vision, prescription) plus Basic Life & AD&D; options for Supplemental life
• Employee Assistance Program (EAP), mental health support, Medical Advice Line
• Flexible Spending Accounts; Adoption and Surrogacy Reimbursement coverage
• 401(k) matching
• Paid time off and parental leave