Amazon Ads is seeking a Data Engineer to support Advertising Demand Enablement & Product Ops (DEPO) in building and improving data pipelines and architectures that turn raw inputs into decision-ready insights at massive scale. The role involves close collaboration with software engineers, data scientists, analysts, and business stakeholders to deliver reliable, performant data solutions.
Role Summary
Design, build, and optimize scalable data engineering solutions for Amazon Ads. You will focus on ETL/ELT pipelines, data modeling, and large-scale data processing while partnering across teams to meet business needs with long-term technical sustainability.
Responsibilities
- Design, build, and maintain scalable ETL/ELT pipelines and data infrastructure, applying best practices for data quality, lineage tracking, and pipeline monitoring to support reliable and performant delivery.
- Create coherent logical and physical data models aligned to business requirements, ensuring data integrity and consistency, and enabling efficient storage and retrieval across relational and distributed systems.
- Develop and optimize SQL queries and scripts to extract, transform, and manipulate large-scale datasets, using distributed processing frameworks for high-volume workloads.
- Collaborate with analysts, scientists, software engineers, and business stakeholders to translate requirements into well-architected data solutions, balancing customer needs with operational sustainability.
- Identify improvements to existing data processes, including automation of manual operations, enhancing data discoverability, and applying generative AI tools where appropriate to increase productivity and improve data product quality.
Required Qualifications
- 4+ years of data engineering experience.
- Experience building or operating highly available, distributed systems for data extraction, ingestion, and processing of large data sets.
- Knowledge of professional software engineering practices across the full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence.
- Knowledge of batch and streaming data architectures such as Kafka, Kinesis, Flink, Storm, and Beam.
- Knowledge of distributed systems as applied to data storage and computing.
- Bachelor’s degree or foreign equivalent in computer science, engineering, analytics, mathematics, statistics, IT, or equivalent.
Technologies
- ETL/ELT
- SQL
- Kafka
- Kinesis
- Flink
- Storm
- Beam
- Hadoop
- Hive
- Spark
- EMR
Preferred Qualifications
- Experience with big data technologies including Hadoop, Hive, Spark, and EMR.
- Experience with non-relational databases and data stores, including object storage, document or key-value stores, graph databases, and column-family databases.
- Experience with and deep understanding of advertising technologies (for example: ad server, DSP).
A Day in the Life
- Review pipeline health and data quality metrics.
- Build or refine ETL workflows that support downstream analytics and machine learning use cases.
- Pair with a data scientist to design a new feature dataset.
- Join design reviews to evaluate proposed architecture changes.
- Mentor teammates on data modeling patterns.
- Participate in code reviews.
- Contribute to team planning and collaborate across functions to align data solutions with evolving business priorities.
About the Team
The team is committed to making data accessible, reliable, and valuable across Amazon Ads. You will help build foundational data infrastructure that enables analysts, scientists, and engineers to make informed decisions at scale. The environment emphasizes collaboration and meaningful ownership of data domains, supported by shared standards that improve engineering practices across the organization.
Compensation and Location
Location: New York, NY (onsite). Salary: USD 145,300 - 196,600 per year.
Benefits
- Sign-on payments and restricted stock units (RSUs).
- Health insurance: medical, dental, vision, prescription, Basic Life & AD&D, and options for Supplemental life plans; EAP and Mental Health Support; Medical Advice Line; Flexible Spending Accounts; Adoption and Surrogacy Reimbursement coverage.
- 401(k) matching.
- Paid time off.
- Parental leave.
