- 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
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
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
- Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift
- Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets
