Data Engineer, Marketing Tech BI & Finance Analytics

Amazon Inc.; Omaha (NE); Hybrid; USD 132,000 - 179,000; Full time

Benefits offered by this job

RSUs

Health insurance

401(k) matching

Paid time off

Job summary

Amazon.com Services LLC is seeking a Data Engineer for Marketing Tech BI and Stores Finance Analytics & Insights. You will own data pipelines, build scalable ETL/ELT processes, and maintain data infrastructure using SQL, Python, and AWS services including Redshift.

You will collaborate with Data Science, BI, Product, and Finance to translate requirements into robust data solutions and empower analytics across teams.

Qualifications

• 3+ years of data engineering experience.
• 1+ years of developing and operating large-scale data structures for business intelligence analytics using ETL/ELT processes.
• 1+ years of developing and operating large-scale data structures for business intelligence analytics using OLAP technologies.
• 1+ years of developing and operating large-scale data structures for business intelligence analytics using data modeling.
• 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).

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 elevate 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.

Skills

Data engineering

ETL/ELT design

OLAP analytics

Data modeling

Python/Scala/Shell

Education

Bachelor's degree in Computer Science, Engineering, Information Systems, Mathematics, or related field

Tools

Hadoop

Hive

Spark

EMR

AWS Redshift/S3

Kinesis

IAM

Job description

Amazon.com Services LLC is seeking a Data Engineer for Marketing Tech BI and Stores Finance Analytics & Insights. You will own data pipelines, build scalable ETL/ELT processes, and maintain data infrastructure using SQL, Python, and AWS services including Redshift.

You will collaborate with Data Science, BI, Product, and Finance to translate requirements into robust data solutions and empower analytics across teams.