Bloomberg Law (BLAW) provides users with fast access to legal content and analysis, practice tools, company information and market intelligence through advanced search & analytic capabilities. We are committed to changing the way legal professionals conduct their day-to-day tasks by automating research and providing analytical solutions to help them get real-time answers and better serve their clients.

The BLAW Engineering Analytics Team is driving the future of legal research by providing a Hadoop & Spark based solution for performing complex analytics for specific client-facing applications, as well as creating a robust platform for all our machine learning needs. You will be joining team of talented engineers to build the big data platform that will be put to use in extracting content and insight from legal information, enabling us to provide advanced visualization to our products, aiding the decision-making for legal research professionals.

We'll trust you to:

• Apply best practices in running large scale distributed systems
• Build a partnership with our product teams and other internal customers to drive the product direction
• Have a passion for analytics, distributed processing, visualization and working with data and databases
• Take risks. Our fast paced environment will require you to rise to the challenge and strive to exceed expectations
• Enforce standards and best practices across all aspects of the platform/product

You'll need to have:

• BA, BS, MS, PhD in Computer Science, Engineering or related technology field
• 3+ years of relevant post-grad industry experience
• Deep understanding of large scale distributed systems/databases
• Solid understanding of different types of data models (relational, columnar, document based, key/value)
• Experience/strong knowledge in Hadoop, Spark, and Large scale data processing
• Experience building data pipelines and services and scaling them
• Knowledge/Experience with web services, API design, OLAP/Business Intelligence, in-memory computing
• Relentless focus on the quality and reliability needed for an infrastructure team