30 days ago
Base Salary
$132k - $179k/yr
Responsibilities
- Build and optimize logical data models and large-scale data ingestion, transformation, and processing pipelines.
- Identify and resolve data quality issues while improving pipeline reliability, maintainability, scalability, security, and cost efficiency.
- Make dataset-level technical trade-offs and contribute to data architecture and infrastructure decisions.
- Write pragmatic, secure, maintainable, and high-quality code and participate in code reviews and design discussions.
- Establish SLAs, automate manual processes, improve data self-service, and drive data engineering best practices.
- Manage operational health through monitoring, alarming, runbooks, on-call participation, and incident resolution.
- Break down project work, deliver independently, collaborate across teams, and resolve competing technical perspectives.
- Mentor peers, participate in hiring, and contribute to team knowledge-sharing.
Requirements
- 5+ years of data engineering experience.
- 3+ years developing and operating large-scale business-intelligence data structures using ETL/ELT processes.
- 3+ years developing and operating large-scale business-intelligence data structures using SQL.
- 5+ years analyzing and interpreting data with Redshift, Oracle, NoSQL, or similar technologies.
- 3+ years developing and operating large-scale business-intelligence data structures using OLAP technologies.
- Experience communicating with users, technical teams, and management to gather requirements and explain software features and technical designs.
- Preferred experience with AWS Redshift, S3, AWS Glue, EMR, Kinesis, Firehose, Lambda, and IAM roles and permissions.
- Preferred experience with non-relational databases and data stores, including object, document, key-value, graph, and column-family stores.
- Preferred experience with Hadoop, Hive, Spark, and EMR.
- Preferred experience with Data and AI technologies, including AI/ML, GenAI, analytics, databases, and storage.
- Preferred 4+ years of experience with data warehouse architectures, data modeling, infrastructure components, ETL/ELT, reporting and analytics tools, data structures, and hands-on SQL coding.
- Experience operating highly available distributed systems for extracting, ingesting, and processing large datasets, or experience with the software development lifecycle.
Benefits
- Comprehensive health insurance including medical, dental, vision, prescription, Basic Life and AD&D insurance, and optional supplemental life plans.
- Employee assistance, mental health support, medical advice line, flexible spending accounts, and adoption and surrogacy reimbursement coverage.
- 401(k) matching, paid time off, and parental leave.
- Amazon package includes sign-on payments and restricted stock units; final compensation depends on experience, qualifications, and location.
- Location: Seattle, Washington, USA.
Tech Stack
Categories
Data Engineering
About Amazon
Amazon builds and operates a global e-commerce marketplace, logistics network, and consumer devices, and provides cloud computing via AWS for businesses and developers. The company earns revenue from online retail, third‑party seller services, subscriptions like Prime, advertising, and AWS usage. Founded in 1994 and headquartered in Seattle, it is publicly traded on NASDAQ (AMZN) and serves customers in dozens of countries.