About the job
Within our Prime Video Global Operations team, we're looking for a results-driven Data Engineer to build and scale the data infrastructure backbone that powers our reporting, analytics, and AI-enabled insights capabilities. In this role, you will design and implement robust data pipelines, integrate data from marketing, business teams, finance, and cross-functional teams. You will also build AI/ML-enabled data solutions and architect the foundational data infrastructure that enables insights, planning, reporting mechanisms, and agentic AI-powered self-service analytics for Amazon PV Global Operations.
Responsibilities
Design, develop, and maintain scalable, automated data pipelines and ETL/ELT processes that ingest, transform, and deliver data to support business reporting and analytics needs.
Architect data infrastructure for agentic AI and Model Context Protocols (MCP) — including structured pipelines, usage data capture, and systems that power AI-enabled self-service analytics and reporting.
Build and maintain data lakes, data warehouses, and APIs ensuring reliable, performant access to clean, well-governed data. Optimize storage, queries, and AWS infrastructure costs.
Create logical data models that drive physical design, enabling BI/analytics teams to build self-service reporting on a solid foundation. Support forecasting and capacity planning at scale.
Establish data quality frameworks, monitoring, and alerting to ensure accuracy, completeness, and freshness. Drive governance best practices including lineage tracking, documentation, and access controls.
Own instrumentation strategy for key platforms, ensuring comprehensive data capture across operational workflows.
Partner cross-functionally with BI engineers, analysts, operations, science, and tech teams to translate data requirements into scalable solutions.
Qualifications
Minimum
Bachelor's degree in business, engineering, statistics, computer science, mathematics or a related field
3+ years of data engineering experience
3+ years of experience with big data technologies such as Hadoop, Hive, Spark, or EMR.
Experience with data modeling, warehousing, and building ETL/ELT pipelines.
4+ years of experience with one or more query languages (e.g., SQL, PL/SQL, DDL, HiveQL, SparkSQL, Scala).
Experience with Python or another scripting language for data processing.
Knowledge of data schema design including normalization, relational models, and dimensional models.
Cross-team collaboration skills and effective written and verbal communication when interfacing with stakeholders, peers, and executives.
Knowledge of professional software engineering best practices for the full software development life cycle, including coding standards, code reviews, source control, continuous deployments, testing, and operational excellence.
Experience using BI tools (e.g., Tableau, QuickSight) to visualize data.
Preferred
Advanced Degree (MS) in engineering, technology, statistics, analytics, or finance.
Experience using BI tools (e.g., Tableau, QuickSight) to visualize data.
Experience developing, scaling, and governing global operations standards and infrastructure across matrixed organizations.
Experience with ETL tools such as Informatica, Airflow, ODI, SSIS, BODI, or Datastage.
Experience architecting/operating solutions built on AWS services including S3, Redshift, SageMaker, EMR, Kinesis, Lambda, and EC2.
Experience in large-scale workforce, operations, or capacity planning functions.
Experience in data mining and working with large-scale, complex datasets in a business environment.
Experience in statistical analysis using tools such as R, SAS, or Matlab.