Amazon Fashion is a fast moving innovative team that is revolutionizing the future of online Fashion retail to become each customer's most loved fashion destination. We are looking for candidates that are passionate about big data and data warehouse technologies (Redshift, EMR, Spark), visualization (Tableau, Amazon QuickSight) and statistical modelling/ML. This role will design, execute, and iterate on managing the data product landscape that is leveraged by the entire in EU Amazon Fashion & Sports organization. This role will drive improvements in buying, forecasting, availability and sell through management to surprise and delight Fashion customers at every turn.
A key responsibility will be managing the technical debt of 20+ data products under the team's umbrella, ensuring operational excellence of data pipelines and web applications that provide critical data and insights to internal customers and selling partners (external). You will also be instrumental in building next generation products using AWS technologies like AWS LakeFormation to enhance our data capabilities and drive innovation.
The ideal candidate has a strong track record and end-to-end ownership of the full stack of data analysis (data engineering, ETL, data modelling, and data visualization), with a high fluency in SQL and Python. You should also thrive on independence and be relentless in finding automated solutions and eliminating manual solutions.
Primary Responsibilities:
- Own Data and BI roadmap by working with business leaders on strategically prioritizing numerous requests and long-term adoption of new technologies
- Develop data products with key actionable metrics for review with senior leadership and to drive action with business teams
- Empower non-technical, internal customers to self-sufficiency in analytics and reporting via business intelligence and visualization software (e.g., Tableau, Amazon Quicksight, AWS Q etc.).
- Maintain and enhance a complex Data lake that powers reporting tools, action-oriented products, ML models and central applications that are widely used by WW business users.
- Use statistical and Machine Learning techniques for strategic deep dives and generate actions, as well as communicating in front of senior leadership.
- Experience in analyzing and interpreting data with Redshift, Oracle, NoSQL etc.
- 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- 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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