Business Intelligence Engineer, Analytics Operations and Programs

AOP team within Amazon Transportation is looking for an innovative, hands-on and customer-obsessed Business Intelligence Engineer for Analytics team. Candidate must be detail oriented, have superior verbal and written communication skills, strong organizational skills, excellent technical skills and should be able to juggle multiple tasks at once.

Ideal candidate must be able to identify problems before they happen and implement solutions that detect and prevent outages. The candidate must be able to accurately prioritize projects, make sound judgments, work to improve the customer experience and get the right things done.

This job requires you to constantly hit the ground running and have the ability to learn quickly. Primary responsibilities include defining the problem and building analytical frameworks to help the operations to streamline the process, identifying gaps in the existing process by analyzing data and liaising with relevant team(s) to plug it and analyzing data and metrics and sharing update with the internal teams.

Key job responsibilities
1) Apply multi-domain/process expertise in day to day activities and own end to end roadmap.
2) Translate complex or ambiguous business problem statements into analysis requirements and maintain high bar throughout the execution.
3) Define analytical approach; review and vet analytical approach with stakeholders.
4) Proactively and independently work with stakeholders to construct use cases and associated standardized outputs
5) Scale data processes and reports; write queries that clients can update themselves; lead work with data engineering for full-scale automation
6) Have a working knowledge of the data available or needed by the wider business for more complex or comparative analysis
7) Work with a variety of data sources and Pull data using efficient query development that
requires less post processing (e. g. , Window functions, virt usage)
8) When needed, pull data from multiple similar sources to triangulate on data fidelity
9) Actively manage the timeline and deliverables of projects, focusing on interactions in the team
10) Provide program communications to stakeholders
11) Communicate roadblocks to stakeholders and propose solutions
12) Represent team on medium-size analytical projects in own organization and effectively communicate across teams
[January 21, 2025, 1:30 PM] Dhingra, Gunjit: Day in life

A day in the life
1) Solve ambiguous analyses with less well-defined inputs and outputs; drive to the heart of the problem and identify root causes
2) Have the capability to handle large data sets in analysis through the use of additional tools
3) Derive recommendations from analysis that significantly impact a department, create new processes, or change existing processes
4) Understand the basics of test and control comparison; may provide insights through basic statistical measures such as hypothesis testing
5) Identify and implement optimal communication mechanisms based on the data set and the stakeholders involved
6) Communicate complex analytical insights and business implications effectively

About the team
AOP (Analytics Operations and Programs) team is missioned to standardize BI and analytics capabilities, and reduce repeat analytics/reporting/BI workload for operations across IN, AU, BR, MX, SG, AE, EG, SA marketplace.

AOP is responsible to provide visibility on operations performance and implement programs to improve network efficiency and defect reduction. The team has a diverse mix of strong engineers, Analysts and Scientists who champion customer obsession.

We enable operations to make data-driven decisions through developing near real-time dashboards, self-serve dive-deep capabilities and building advanced analytics capabilities.

We identify and implement data-driven metric improvement programs in collaboration (co-owning) with Operations teams. - 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience with data modeling, warehousing and building ETL pipelines
- Experience in Statistical Analysis packages such as R, SAS and Matlab
- Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling