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Senior Reporting Engineer

at in San Francisco, CA   —   Jul 01, 2014   |  
Want to be a part of building the data warehouse behind the most intelligent job matching system on the planet? We are seeking a highly motivated ETL Engineer to help lead our overall Business Intelligence efforts, focusing on building data stores that are consumed by both internal/external reporting systems and our core product.

Bright harnesses the power of Big Data to help eliminate the noise in the hiring process - efficiently connecting job seekers to their best opportunities, and employers to their top prospects.

Our ideal candidate is passionate about working with extremely large volumes of data and is an expert of ETL techniques and best practices. They must have at least 5+ years of ETL development experience with Python, Perl, PHP or similar technologies, as well as at least 4+ years of experience working with Pentaho (Kettle) and Mondrian (OLAP). Lastly, they should have proven experience working with data warehousing architecture and data modeling.

If you enjoy using massive volumes of data to help drive business decisions, apply today at:

Learn more about what it's like to work at Bright:

Bright is proud to be an EOE/M/F/D/V
Build data solutions that help product and business teams make data driven decisions
Rethink and influence strategic and roadmap decisions for building scalable data solutions and scalable data warehouse environments
Interface closely with data infrastructure and engineering teams to build and extend cross platform ETL and reports generation framework
Provide consultative solutions approach to business partners such as Analysts, Management, End Users and Developers to clarify objectives, determine scope, drive consensus, identify problems and recommend solutions
Support end users on ad hoc data usage and be a subject matter expert on functional side of the business
B.S. or M.S. Computer Science or related field
Knowledge of Hadoop, HBase and Hive highly preferred
Knowledge of optimizing relational data stores for large datasets is preferred
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