Bloomberg is the global leader in business and financial data, news, and insight. Using the power of technology, we connect the world’s decision makers to accurate information on the financial markets – and help them make faster, smarter decisions.

Bloomberg Portfolio Analytics (PORT) empowers the biggest players in the financial world to manage their portfolios, assess exposures, and make decisions that move the markets. As a flagship product on the Bloomberg Terminal, our mission-critical tools are used daily by money managers, mutual funds, hedge funds, and pension funds around the world. PORT provides industry-leading quantitative financial tools, and our enterprise reporting system produces hundreds of thousands of reports daily. We are experiencing tremendous growth of our products and user base, and we are constantly looking to innovate upon our existing software and technologies.

PORT Engineering is a highly-collaborative, globally-distributed department. The San Francisco-based Data Transparency team applies modern data science approaches to improve client access to PORT’s curated data sets and sophisticated financial models. We use technologies such as Jupyter notebooks and Apache Spark to support PORT systems and improve data quality across our application stack.

The team’s primary product is the Portfolio Enterprise Data Lake (PEDL), an Apache Parquet-based store that integrates PORT’s enterprise reporting workflow with our domain-specific Bloomberg Query Language (BQL) and other APIs. Customers are using our platform to produce highly-customized reports and perform business intelligence-style analysis. We are growing the PEDL platform to cover additional client use cases as we scale up to an ever-increasing amount of data.

We have:

• Unique, compelling datasets curated by Bloomberg over decades of partnerships with the world's leading financial institutions
• Tens of terabytes of constantly growing data across various stores at the core of our platform that delivers hundreds of thousands of reports daily
• Supportive colleagues, many with significant involvement in the open source projects we use in our products
• An inclusive employee community, offering frequent technical training and professional development opportunities

We'll trust you to:

• Apply your practical experience with large data sets to the challenges of our dynamic environment
• Collaborate with teammates locally and around the globe to influence long-term development of PORT architecture and simplify complex data pipelines
• Work closely with internal and external clients, influence product design, and own your contributions from development to deployment and beyond
• Be comfortable with both compiled and interpreted programming languages, and switching between them as necessary

You need to have:

• 5+ years professional programming experience with Java, Python, or comparable languages
• Experience with large, scalable distributed systems
• Knowledge of data structures and understanding of algorithms
• Pragmatic problem solving skills
• BA, BS, MS, or PhD in Computer Science, Engineering or related technology field, or equivalent experience

We'd love to see:

• Experience with disk-backed data store design patterns and building infrastructure services
• Knowledge of data formats like Apache Parquet and Apache Arrow, and their associated APIs in Java and Python
• Use of Docker-based workflows
• Familiarity with Apache Spark, Jupyter notebooks, Apache Kafka, and Kubernetes
• Interest in the financial markets

While these are examples of skills that will help you be successful in this role, they are not exhaustive or comprehensive, and we encourage you to apply if you believe this role is a great next step for your career.

We recently posted a "meet the team" blog post, check out the link below to get to know our SF engineering office!

• https://www.techatbloomberg.com/blog/meet-the-teams-sf-engineering/

Bloomberg is an equal opportunities employer, and we value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.