Job Description
Amazon’s Supply Chain Transportation Planning team is responsible for figuring out how goods move efficiently between fulfilment centres, sort centres and delivery stations across the region, and this Data Analyst role exists to turn the mountain of shipment, cost and network data that process generates into decisions people can actually act on. It’s a role built for someone who likes big, messy datasets and wants to see their analysis change how freight actually moves.
Day to day, the work involves pulling and cleaning large transportation and supply chain datasets, building models and reports that planners and managers rely on, and working cross-functionally with operations, finance and network design teams to answer questions like where capacity is tight, where cost is creeping up, or where a lane needs rethinking. Expect a mix of hands-on SQL and spreadsheet work alongside more analytical modelling, plus regular communication of findings to stakeholders who are not analysts themselves.
It suits someone with a quantitative background — data analysis, supply chain, statistics, engineering, or similar — who is comfortable with big data handling and wants exposure to how a transportation network actually runs at Amazon’s scale. Curiosity about logistics and a willingness to get into the operational detail behind a number, rather than just reporting it, both matter here.
For someone building a career in supply chain analytics or data science, this is a chance to work on transportation planning problems at a scale and complexity that few companies outside Amazon can offer in the region.
Impact of the Job
Transportation is one of the largest cost and performance levers in any supply chain, and the decisions this role informs — where to add capacity, which lanes are inefficient, how cost is trending across the network — have a direct effect on both Amazon's delivery speed and its cost base in the region. A Trans Planning Data Analyst turns raw shipment, cost and network data into the kind of clear, decision-ready analysis that planners and leaders use to make real network changes, which means the quality of this analysis has a multiplier effect: a well-identified bottleneck or cost anomaly can save far more than the analyst's own time once it's fixed at network scale. Because transportation planning sits upstream of fulfilment and delivery, getting this analysis wrong, or getting it too slowly, can quietly degrade delivery performance long before the symptoms show up anywhere customer-facing. In a business where speed and cost efficiency are core competitive levers, this kind of rigorous, data-grounded transportation analysis is a genuine driver of how well Amazon's regional network performs, not a back-office reporting function.
Job Seeker Benefit
Working as a data analyst inside Amazon's Supply Chain Transportation Planning function is a strong way to build genuinely advanced analytics skills on problems that matter at scale: large dataset handling, SQL, network-level modelling, and the kind of cross-functional communication that turns a spreadsheet into an actual decision. Few companies in the region offer transportation networks of Amazon's size and complexity to analyse, so the technical depth gained here — dealing with real big data volumes, messy operational inputs, and competing cost and service trade-offs — is hard to replicate elsewhere. This role also builds a practical understanding of supply chain and logistics economics that is valuable well beyond Amazon, since transportation and network planning skills transfer directly into roles at other logistics companies, retailers with their own delivery networks, and consulting firms serving the sector. The combination of technical analytics skill and operational supply chain fluency is a particularly strong one for career progression, since it opens paths into senior analyst, supply chain manager, or even transportation strategy roles over time. Add to that Amazon's reputation for data rigor and structured decision-making, and this experience carries real weight on a resume aimed at analytics, operations research, or supply chain leadership careers going forward.
Suggested Resume Keywords
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Requirements
- Bachelor’s degree in a quantitative field such as supply chain, statistics, engineering, economics, or data science
- Experience working with large, complex datasets (big data handling)
- Proficiency in SQL and advanced Excel; familiarity with a BI or visualisation tool is a plus
- Strong analytical and problem-solving skills with attention to data accuracy
- Ability to communicate data-driven findings clearly to non-technical stakeholders
- Cross-functional collaboration skills, working with operations, finance and network teams
- Interest in supply chain, transportation, or logistics operations
Interview Questions to Prepare For
Walk me through a time you used data to identify an inefficiency others had missed.
Checks for genuine analytical curiosity versus purely reactive reporting.
How do you approach cleaning and validating a large, messy dataset before analysis?
Tests practical data-handling discipline.
Describe a time you had to explain a complex analysis to someone without a data background.
Probes communication skills critical for stakeholder-facing analytics roles.
What SQL or data tools are you most comfortable with, and how have you used them in a supply chain or operations context?
Tests technical fit and real-world application.
Tell me about a time your analysis led to an actual operational change.
Checks for impact orientation, not just analysis for its own sake.
How would you prioritise competing requests from operations, finance and network teams for the same data?
Tests stakeholder management and prioritisation.
What interests you specifically about transportation and supply chain data versus other types of analytics work?
Checks genuine domain interest versus a generic analytics application.