Job description

Mkt Tech BI team owns one of the largest datasets at Amazon, our team has various backgrounds that provide you the opportunity to learn each other. Our ultimate goal is to build a robust/scalable data infrastructure and automated reporting system to empower users have maximum flexibility to play with the data in order to maximize Long Term Free Cash Flow(LTFCF). The ideal candidate relishes working with large volumes of data, enjoys the challenge of highly complex technical contexts, and, above all else, is passionate about data and analytics. They are an expert with data modeling, ETL design and business intelligence tools and passionately partners with the business to identify strategic opportunities where improvements in data infrastructure creates out-sized business impact. He/she is a self-starter, comfortable with ambiguity, able to think big (while paying careful attention to detail), and enjoys working in a fast-paced and global team. It's a big ask, and we're excited to talk to those up to the challenge! Key job responsibilities - Own the design, development, testing, deployment, and operation of data pipelines and datasets within an assigned domain - Build and maintain scalable ETL/ELT workflows using SQL, Python, AWS services, and big data technologies - Operate and improve data infrastructure, including Redshift clusters, data lake tables, orchestration workflows, monitoring, alerting, and data quality controls - Improve operational reliability by identifying recurring failures, reducing manual intervention, automating recovery steps, and creating clear runbooks - Partner with Data Science, Business Intelligence, Product, Finance, Engineering, Privacy, and Legal stakeholders to translate business and compliance requirements into scalable data solutions - Build and operate conversational, self-service, and agentic analytics data products - Contribute to data foundations that support forecasting, experimentation, ML/AI use cases, self-service analytics, and certified business metrics - Implement data validation, lineage, documentation, and operational mechanisms that improve trust and reduce single points of failure - Drive scoped modernization efforts such as pipeline simplification, migration support, Redshift/data lake improvements, automation, and self-service data enablement - Clarify ambiguous requirements, identify data quality or source-of-truth gaps, and escalate broader trade-offs to senior engineers or managers when appropriate - Mentor junior engineers on scoped technical tasks, coding standards, operational practices, and data quality expectations - Participate in on-call and product support for business-critical pipelines and datasets - Own the design and operation of the data foundations that power GenAI, RAG, and agentic analytics within an assigned domain - Build guardrails, validation, and evaluation mechanisms, both automated and human-in-the-loop, that keep AI-generated outputs such as SQL and metrics accurate and reliable - Apply AI coding assistants and agentic development tools to your daily work and share effective patterns with the team to raise overall engineering velocity. - 3+ years of data engineering experience - 1+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience - 1+ years of developing and operating large-scale data structures for business intelligence analytics using OLAP technologies experience - 1+ years of developing and operating large-scale data structures for business intelligence analytics using data modeling experience - Bachelor's degree or foreign equivalent in Computer Science, Engineering, Information Systems, Mathematics, or a related field - Experience with data modeling, warehousing and building ETL pipelines - Experience with big data technologies such as: Hadoop, Hive, Spark, EMR - Experience with one or more scripting language (e.g., Python, KornShell, Scala) - 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 AI/ML technologies Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit amazon.jobs for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including 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, and parental leave. Learn more about our benefits at amazon.jobs . USA, WA, Seattle - 132,100.00 - 178,800.00 USD annually

Requirements

Must-haves

Key job responsibilities
Own the design, development, testing, deployment, and operation of data pipelines and datasets within an assigned domain
Build and maintain scalable ETL/ELT workflows using SQL, Python, AWS services, and big data technologies
Operate and improve data infrastructure, including Redshift clusters, data lake tables, orchestration workflows, monitoring, alerting, and data quality controls
Improve operational reliability by identifying recurring failures, reducing manual intervention, automating recovery steps, and creating clear runbooks
Partner with Data Science, Business Intelligence, Product, Finance, Engineering, Privacy, and Legal stakeholders to translate business and compliance requirements into scalable data solutions
Build and operate conversational, self-service, and agentic analytics data products
Contribute to data foundations that support forecasting, experimentation, ML/AI use cases, self-service analytics, and certified business metrics
Implement data validation, lineage, documentation, and operational mechanisms that improve trust and reduce single points of failure
Drive scoped modernization efforts such as pipeline simplification, migration support, Redshift/data lake improvements, automation, and self-service data enablement
Clarify ambiguous requirements, identify data quality or source-of-truth gaps, and escalate broader trade-offs to senior engineers or managers when appropriate
Mentor junior engineers on scoped technical tasks, coding standards, operational practices, and data quality expectations
Participate in on-call and product support for business-critical pipelines and datasets
Own the design and operation of the data foundations that power GenAI, RAG, and agentic analytics within an assigned domain
Build guardrails, validation, and evaluation mechanisms, both automated and human-in-the-loop, that keep AI-generated outputs such as SQL and metrics accurate and reliable
Apply AI coding assistants and agentic development tools to your daily work and share effective patterns with the team to raise overall engineering velocity.
3+ years of data engineering experience
1+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes experience
1+ years of developing and operating large-scale data structures for business intelligence analytics using OLAP technologies experience
1+ years of developing and operating large-scale data structures for business intelligence analytics using data modeling experience
Bachelor's degree or foreign equivalent in Computer Science, Engineering, Information Systems, Mathematics, or a related field
Experience with data modeling, warehousing and building ETL pipelines
Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
Experience with one or more scripting language (e.g., Python, KornShell, Scala)

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

sqlpythonawsmlai

Platform: amazon
Effort: medium

Benefits

Benefits detected in the listing.

Health Insurance

Dental

Vision

401(K)

Paid Time Off

Parental Leave