The Data Engineer leads shopkick’s data collection and warehousing efforts. S/he owns the design and implementation of a data warehouse to store mobile usage, web site, and operational data, gathering data into the warehouse, and analysis of the data. The Data Engineer is an interdisciplinary individual who works closely with many stakeholders to ensure that the data most important to analyzing and improving shopkick’s operations is accessible and well-understood.
Shopkick is the largest real-world shopping app. It lets users treat themselves while shopping. All they need to do is walk
into stores, and they earn points (like miles) that are called kicks, just for walking in. No purchase is necessary. In addition, shopkick shows users great products at stores, and rewards users for engaging with products at stores. Within three years from launch, 6+ million users signed up in the U.S., as well as 13 retail partners at over 7,000 large stores in the U.S., including Target, Macy’s, Old Navy, Best Buy, Crate & Barrel, American Eagle, Sports Authority, Simon Malls and others, and 75+ brands (P&G, Unilever, Kraft, Levi’s, HP, Intel).
Shopkick’s vision is to fundamentally transform shopping at physical stores for consumers by making it more rewarding, entertaining and personal, using mobile phones’ location awareness, unprecedented personalization capabilities, and social/viral features. It is based in Redwood City, founded by mobile and consumer experts, and funded with $20M (Series A and B) by Kleiner Perkins and Reid Hoffman/Greylock (founder and Chairman of LinkedIn, and Partner at Greylock).
Shopkick addresses the no. 1 issue of all physical retailers: driving foot traffic. Fast Company named shopkick one of the Top Ten Most Innovative Companies in Retail by Fast Company, along with Amazon.com, Apple and Starbucks. The Wall Street Journal ranked shopkick one of the Top 10 Apps among 500,000 on Apple’s App Store. Visa and MasterCard became strategic partners for purchase verification, so shopkick can now reward users for walking into stores as well as for purchases. Role & Responsibilities:
- Design data warehouse and datamarts.
- Work with product teams to ensure that the requisite data is recorded, and to extract data incrementally from its sources.
- Work with business owners to identity and provide key metrics.
- Create ETL processes and ensure that data is properly stored.
- Design queries and analytic processes.
- Write/deploy web-based reporting tools allowing stakeholders to access data.
- Provide regular reports to company stakeholders.
- You have a “feel” for numbers, and a passion for achieving a quantitative understanding of complex interactions.
- You have a strong, detailed understanding of business requirements – numbers mean something to you.
- You apply a great deal of creativity to presenting complex quantitative information in a way that brings insights to the fore.
- You’re open to using unconventional tools and approaches. It doesn’t faze you that some data might come from a traditional SQL database, while other data might come from log files analyzed using Hadoop, or from third-party tools. You’re smart about using what is available, but able to roll your own.
- 5+ years of experience in engineering; BS CS/MIS or equivalent.
- Good working understanding of statistics.
- Deep experience in star schema design, ETL, and extracting data from obscure sources; expert-level knowledge of SQL.
- Experience with analysis package(s) and practices.
- Experience with large-scale web site analysis and/or advertising optimization strongly preferred.
- Experience working with open source software tools and stacks preferred.
- Experience with Map/Reduce framework(s) preferred.
- Expertise in Python is a plus.