Day Shift Amazon Python Jobs in Glendale, AZ (NOW HIRING)

Amazon's North America Supply Chain moves millions of packages every day through a network of over 300 fulfillment and sort centers. The Production Planning team is responsible for deciding how many people we need, where we need them, and when - across every site, every shift, every week. Getting this right means customers get packages on time.

Getting it wrong means millions in wasted labor or missed delivery promises.
We are looking for a Business Intelligence Engineer who will build the analytics backbone that powers these planning decisions. You will not just write queries and build dashboards. You will own the data infrastructure that hundreds of planners and operators rely on every single week to make headcount and capacity decisions across the network

Your work will directly influence how Amazon allocates tens of thousands of labor hours and millions of dollars in workforce spend.
This is a high ownership role. You will take ambiguous business questions, figure out where the data lives, build the pipeline to transform it, deliver it in a format stakeholders can act on, and then measure whether it actually drove better decisions. You will operate at the intersection of data engineering, business intelligence, and operational planning at a scale few companies can offer.
Key job responsibilities
Own the development and maintenance of dashboards and automated reports that surface critical production planning metrics to stakeholders across the NASC network.
Collaborate with cross functional business teams including NASC Operations, Workforce Staffing, Network Design and Planning, and Regional Planning to translate workforce planning questions into analytical frameworks and deliver actionable insights that drive real decisions.
Design, build, and deploy data pipelines using Python, SQL, AWS Lambda, Athena, and Redshift

Monitor pipeline health proactively. Ensure reliable and timely data delivery aligned to weekly planning cadences where a one day delay means planners are flying blind.
Build and maintain curated datasets that serve as the single source of truth for capacity planning across the network. This includes a centralized data lake with well defined schemas, partition strategies, lineage documentation, and freshness SLAs that multiple downstream consumers depend on.
Identify and implement automation opportunities that reduce manual data processing

Every hour a planner spends pulling data manually is an hour they are not spending on strategic planning decisions. Your job is to eliminate that waste through scalable self service solutions.
Maintain clear documentation of data sources, business logic, transformation rules, and dataset schemas.
A day in the life
As a Business Intelligence Engineer, you'll monitor pipeline health and resolve data quality issues to keep analytics reliable. You'll partner with operations leadership to scope new requests, translating business questions into analytical solutions

You'll design and refine data products: dashboards, models, and self-service tools while iterating quickly on stakeholder feedback. You'll collaborate cross-functionally to validate the datasets that power planning decisions, ensuring accuracy and trust. And you'll continuously improve your work by documenting transformation logic, automating manual processes, and proactively surfacing recommendations that help business leaders make confident, data-driven decisions.
About the team
The PPT BI team is the analytics function within North America Supply Chain's production planning organization

We build data pipelines, automated reports, and self service dashboards that help planners and operations leaders make workforce and capacity decisions across a large network of fulfillment and sort centers. We are a small team with high ownership where you operate end to end - from requirements to production. You will partner with operations, staffing, network design, and regional planning teams on work that directly influences how labor is allocated across the network.