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Tamr is the enterprise-scale data unification company trusted by industry leaders like GE, Toyota, Thomson Reuters, and GSK. The companys patented software platform uses machine learning supplemented with customers knowledge to unify and prepare data across myriad silos to deliver previously unavailable business-changing insights. With a co-founding team led by Andy Palmer (founding CEO of Vertica) and Mike Stonebraker (Turing Award winner) and backed by founding investors NEA and GV, Tamr is transforming how companies get value from their data.
As we rapidly grow our engineering team, we are seeking for outstanding engineers interested in building products that solve customer problems & enable customer innovation. This is an opportunity to build enterprise software that solves the most time-consuming, manually intensive problem in data science and modern data pipelines: enriching, curating, and preparing data. As part of a talented team, you will design and implement creative solutions to problems in data pipelining, distributed computing, cloud computing, machine learning and user experience.
This job might be a good fit for you if you have:
- At least 10 years of industry experience in software development
- Strong computer science fundamentals. BS or MS in Computer Science preferred. Equivalent industry experience acceptable
- Strong experience with Java or any other modern programming language
- Experience with Big Data technologies, Distributed systems or Cloud native software
- Excellent problem-solving skills
- A collaborative working style
- A hunger to learn new things and achieve big outcomes
This position is available in Cambridge MA. Tamr does sponsor employees requiring a visa.
Tamr provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, disability, genetic information, marital status, amnesty, or status as a covered veteran in accordance with applicable federal, state and local laws.
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