TellApart unlocks the power of customer data for many of the world's largest retailers. Our sophisticated distributed systems power a real-time customer scoring platform (for 100s of millions of consumers in real time) that allows us to make data driven decisions to lift our retailers' revenue by double digit percentage points. Our strengths are building web-scale infrastructure, handling massive data pipelines, and building systems leveraging machine learning to make intelligent decisions.
TellApart is backed by Greylock Partners and Bain Capital Ventures. Several of technology’s top executives have also invested in TellApart, including: Ron Conway (SV Angel), Dick Costolo (CEO, Twitter), Reid Hoffman (Founder & Chairman, LinkedIn), Jeff Jordan (former CEO, OpenTable), Phil Libin (CEO, Evernote) and Mike Walrath (former CEO, RightMedia). David Rosenblatt (former CEO, DoubleClick) is also an executive advisor.
Our team’s impact on the business:
The engineering team at TellApart creates big data solutions that collect and analyze petabytes of shopping data from some of the largest retailers in the world. Built with Amazon S3, Thrift, Hadoop, Hive, EMR, Azkaban and Cascading (as well as a huge set of internal tools), we have successfully enabled the TellApart story so far.
Why we need you:
Play a role in scaling our existing data pipeline to handle 10x the data to match our current growth trajectory
Build a real-time data pipeline that produces up-to-the-minute analyses
Participate in next-generation hardware choices and configurations for our Hadoop clusters to optimize performance and reliability
Build or choose open source tools to help other engineers access complex data more efficiently
Most importantly, because you are excited by big data technologies and would like to make meaningful contributions toward the next advances in big data
Examples of projects recently undertaken by our team:
We needed to make it easy for any engineer to query customer data, so we created a framework on top of Cascading that exposes a uniform interface for extracting data from raw server logs
As the complexity of our job flow grew, we designed and built a reporting workflow using Azkaban running over a cost-efficient Hadoop cluster using Amazon’s Elastic MapReduce
We hack into open source projects to improve reliability and squeeze the last bit of performance out
Currently enrolled and studying toward Computer Science or related degrees from a well-recognized university with a strong GPA (graduate students are welcome to apply)
Academic projects (or personal research) into large-scale distributed systems and big data systems and/or using big data for data analysis
Good understanding of distributed system concepts used in scaling big data technologies with exponential growth of data and speeding up queries
Some understanding of the inner workings of one or more of the big data technologies like (but not limited to) Hadoop, Hive, Cascading, Azkaban, HBase, Pig, Oozie
Excellent analytical skills to deliver meaningful and impact-driven insights using big data
Ability to thrive in a dynamic, fast-paced, collaborative, and high-growth start-up environment
Excellent communication skills, initiative, and teamwork
Our data is unique, our platform is full of possibilities, and our business is booming. We are looking for motivated talent who want to take our technology and business to the next level and realize its full potential. If this sounds like your kind of environment, come join us at TellApart! Please apply and include any links to code samples (if possible/relevant).
TellApart is an Equal Employment Opportunity and Affirmative Action Employer with a commitment to workplace diversity. All qualified individuals are welcome to apply. Employment with TellApart is based solely upon one's individual merit and qualifications directly related to professional competence. We do not discriminate on the basis of race, religion, color, sex, age, national origin, citizenship, or disability. And we will make all reasonable accommodations to meet our obligations under the Americans with Disabilities Act (ADA) and state disability laws.