Data Engineer II, AWS DC Global Network Delivery
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AWS Global Network Delivery Services keeps the cloud running. We own the delivery and operation of all AWS global infrastructure — every data center, every server, every cable that gives hundreds of millions of customers uninterrupted access to the services they depend on. We tackle some of the hardest supply chain and infrastructure problems on the planet, with thousands of variables in play, and we're looking for exceptional people to help solve them.
You'll join a diverse, cross-functional team — Product, BI, network engineers, planners, builders, and operations leaders worldwide — collaborating across AWS to deliver the highest standards of safety and security while providing seemingly infinite capacity at the lowest possible cost. You'll work in an inclusive culture that welcomes bold ideas and empowers you to own them to completion.
We're seeking a talented Data Engineer who thrives with a startup mentality — someone who moves fast, thinks big, and partners directly with business and senior leaders to turn ambiguous problems into clear, data-backed recommendations.
What makes this role exciting? You won't just move data from point A to point B — you'll build context through your pipelines. You'll own the context-driven source-of-truth tables that track the infrastructure delivery process across its full lifecycle — from intake through deployment to decommission — fueling analytics, LLMs, and business decisions. You'll manage the infrastructure behind those pipelines, keeping it lightweight, auditable, and highly available. And you'll partner closely with Data Engineering, Product, and Software teams to design pipelines that let business teams answer their own questions — without waiting in a queue.
The ideal candidate is passionate about understanding the business and making an impact with data. You ask sharp questions, design thoughtful data models, and build relevant pipelines with low tech debt. You know great data engineering isn't about shipping pipeline after pipeline — it's about data quality, governance, and knowing your sources well enough to make the right trade-offs. You'll build well-documented, context-rich datasets and self-service tools (new or reused) that put data directly in the hands of the people who need it most — applying data mining, data modeling, and AI to evaluate business health and drive measurable improvement..
Key job responsibilities
- Know why you're building, not just what. Ask sharp questions first, understand your data and its significance, reuse before you rebuild, and set clear data contracts so data is always traceable to its source — and correct.
- Own the design, development, and operation of scalable, production-grade, context-driven data pipelines — including the infrastructure behind them — keeping them lightweight, cost-efficient, auditable, and highly available to power metrics, reporting, analytics, and ML/LLM use cases.
- Build context-rich "source of truth" datasets and reusable, dimensional data models that track the infrastructure delivery lifecycle (intake → deployment → decommission), serving as a trusted, intuitive-to-query foundation for downstream consumers while keeping tech debt in check.
- Design data models and ETL/ELT workflows to ingest, transform, and integrate data from diverse sources using AWS services (e.g., S3, Redshift, Glue, EMR, Athena, Lambda) and internal Amazon data platform tools.
- Define and champion data quality/ engineering and governance best practices — validation, monitoring/alerting, testing, design standards, documentation, and governance — to keep pipelines accurate, trustworthy, and well-maintained.
- Partner with Software, Product, and other Data Engineering teams to source and model the right business entities, so any team can answer their own questions without waiting in a queue.
- Troubleshoot operational and data-quality issues with root-cause analysis and durable fixes; audit existing pipelines, jobs, and queries, and recommend improvements to upstream sources for greater accuracy, simplicity, and scalability
About the team
We are the PAX Data and Analytics team — a small, high-conviction group of BI Engineers and Data Engineers embedded in one of the world's largest and fastest-growing data center organizations. We were established less than a year ago with a clear mandate: **turn operational complexity into decision-ready clarity.**
We don't build data pipelines and dashboards . We build *context* — the kind that lets leaders act with confidence and operators spot problems before they become incidents. Our work spans the full data lifecycle: sourcing and integrating raw operational signals, engineering relevant and reliable pipelines, and surfacing insights through self-service tools that hundreds of people rely on daily.
Every pipeline we ship, every standard we define, and every contract we establish becomes the foundation for what comes next
We are young, we are scrappy, and we are building something that will outlast us. If you want to lay foundations that an entire organization will stand on — you're in the right place.
You'll join a diverse, cross-functional team — Product, BI, network engineers, planners, builders, and operations leaders worldwide — collaborating across AWS to deliver the highest standards of safety and security while providing seemingly infinite capacity at the lowest possible cost. You'll work in an inclusive culture that welcomes bold ideas and empowers you to own them to completion.
We're seeking a talented Data Engineer who thrives with a startup mentality — someone who moves fast, thinks big, and partners directly with business and senior leaders to turn ambiguous problems into clear, data-backed recommendations.
What makes this role exciting? You won't just move data from point A to point B — you'll build context through your pipelines. You'll own the context-driven source-of-truth tables that track the infrastructure delivery process across its full lifecycle — from intake through deployment to decommission — fueling analytics, LLMs, and business decisions. You'll manage the infrastructure behind those pipelines, keeping it lightweight, auditable, and highly available. And you'll partner closely with Data Engineering, Product, and Software teams to design pipelines that let business teams answer their own questions — without waiting in a queue.
The ideal candidate is passionate about understanding the business and making an impact with data. You ask sharp questions, design thoughtful data models, and build relevant pipelines with low tech debt. You know great data engineering isn't about shipping pipeline after pipeline — it's about data quality, governance, and knowing your sources well enough to make the right trade-offs. You'll build well-documented, context-rich datasets and self-service tools (new or reused) that put data directly in the hands of the people who need it most — applying data mining, data modeling, and AI to evaluate business health and drive measurable improvement..
Key job responsibilities
- Know why you're building, not just what. Ask sharp questions first, understand your data and its significance, reuse before you rebuild, and set clear data contracts so data is always traceable to its source — and correct.
- Own the design, development, and operation of scalable, production-grade, context-driven data pipelines — including the infrastructure behind them — keeping them lightweight, cost-efficient, auditable, and highly available to power metrics, reporting, analytics, and ML/LLM use cases.
- Build context-rich "source of truth" datasets and reusable, dimensional data models that track the infrastructure delivery lifecycle (intake → deployment → decommission), serving as a trusted, intuitive-to-query foundation for downstream consumers while keeping tech debt in check.
- Design data models and ETL/ELT workflows to ingest, transform, and integrate data from diverse sources using AWS services (e.g., S3, Redshift, Glue, EMR, Athena, Lambda) and internal Amazon data platform tools.
- Define and champion data quality/ engineering and governance best practices — validation, monitoring/alerting, testing, design standards, documentation, and governance — to keep pipelines accurate, trustworthy, and well-maintained.
- Partner with Software, Product, and other Data Engineering teams to source and model the right business entities, so any team can answer their own questions without waiting in a queue.
- Troubleshoot operational and data-quality issues with root-cause analysis and durable fixes; audit existing pipelines, jobs, and queries, and recommend improvements to upstream sources for greater accuracy, simplicity, and scalability
About the team
We are the PAX Data and Analytics team — a small, high-conviction group of BI Engineers and Data Engineers embedded in one of the world's largest and fastest-growing data center organizations. We were established less than a year ago with a clear mandate: **turn operational complexity into decision-ready clarity.**
We don't build data pipelines and dashboards . We build *context* — the kind that lets leaders act with confidence and operators spot problems before they become incidents. Our work spans the full data lifecycle: sourcing and integrating raw operational signals, engineering relevant and reliable pipelines, and surfacing insights through self-service tools that hundreds of people rely on daily.
Every pipeline we ship, every standard we define, and every contract we establish becomes the foundation for what comes next
We are young, we are scrappy, and we are building something that will outlast us. If you want to lay foundations that an entire organization will stand on — you're in the right place.
