Operations Engineer, NA Capacity Engineering

- Bachelor's degree
- 2+ years of experience in a role where real-world constraints need to be considered or modelled and data/metrics are used to justify change
- Knowledge of Microsoft Office products and applications (Excel, PowerPoint, Word, Visio, and MS Project) at an advanced level
- Bachelor's degree in industrial or manufacturing engineering
- Bachelor's degree in engineering, statistics, computer science, operations research, business analytics, information systems or equivalent
- Knowledge of AutoCAD, VBA, and SQL at the intermediate level
- Experience communicating with senior leaders both verbally and through written narratives

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $68,900/year in our lowest geographic market up to $126,400/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.
Amazon is seeking an Operations Engineer to support the Amazon Logistics North America (AMZL NA) Capacity Engineering team. This role will manage the capacity of multiple last mile sites and partner with Topology, Operations, Design/Process/Network Engineering, Finance, and other planning teams to build a reliable view of short/mid/long term capacity. In this role, the Operations Engineer will drive innovation in developing the future of delivery operations to better serve customers and maximize the utilization of Amazon assets by increasing network capacity with frugal solutions. This is a high visibility role working with senior leaders to enable strategic and tactical decision-making.

Key job responsibilities
- Support day to day execution for a subset of North America AMZL sites and take ownership to quickly remove constraints and barriers
- Develop 24-hour operations clocks which optimize the use of time while balancing competing objectives
- Influence building design decisions through high-judgment evaluation of constraints
- Drive capacity alignment between capacity planning tools and other tools & systems
- Write effective documents for diverse audiences, including senior leaders
- Serve as an ambassador for the Capacity Engineering team, helping others understand our space and identifying opportunities for collaboration
- Write effective documents for diverse audiences, including senior leaders
- Actively drive progress across multiple efforts in parallel, independently managing priorities and relationships with other teams to effectively deliver results

About the team
The Capacity Engineering team is a key stakeholder in efforts across AMZL which may impact the network's capacity or utilization; this means we work cross-functionally to contribute critical analysis for strategic decisions.

Our mission is to ensure AMZL scales to the right capacity in the right place at the right time, and that we are continuously innovating to improve speed for customers and reduce costs to deliver.

The team is highly collaborative and we move quickly to remove barriers for our customers. We are advocates for objective and data-driven models of the real world, but seek constant feedback (anecdotal and data) to better predict the ground truth.