IN Reach - Analytics and Science Team (AST) has a vision to embed a data culture deeply in our IES Shopping Experience organization, fostering invention through insights, and building a robust data architecture to support business needs. We spin the insights flywheel by growing a pool of bar-raisers and diverse data professionals, which empowers us to continuously enhance our data capabilities, holistically covering disciplines of Data Engineering, Business Intelligence, Analytics, and Machine Learning.
We are looking for a candidate that demonstrated success working cross-functionally across internal and external teams. This candidate must have a track record of churning out actionable insights and make data backed recommendations that directly impact organizational strategic decisions and priorities. Being able to thrive in an ambiguous, fast-moving environment and prioritizing work is essential, as is a mind for innovation and learning through new technologies. This role provides an opportunity to develop original ideas, approaches, and solutions in a competitive and ever-changing business climate.
Key job responsibilities
- Conduct deep dive analyses of business problem statements and formulate conclusions and recommendations to leadership
- Share written recommendations and insights for key stakeholders that will help shape organizational strategic decisions and priorities
- Contribute to the design, implementation, and delivery of BI solutions for complex and ambiguous problems
- Simplify and automate reporting, audits, and other data-driven activities
- Partner with other BIEs to enhance data infrastructure, data availability, and broad access to customer insights
- Develop and drive best practices in data integrity, consistency, analysis, validations, and documentation
- Learn new technology and techniques to meaningfully support internal stakeholders and process innovation
- 5+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
- Knowledge of SQL and data warehousing concepts
- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with forecasting and statistical analysis
- Master's degree in BI, finance, engineering, statistics, computer science, mathematics, finance or equivalent quantitative field
- 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
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