Senior Data Management Professional - Data Quality Engineer - Ownership Data

Bloomberg•New York, NY

65d•$110,000 - $190,000

About The Position

Bloomberg runs on data. Our products are fueled by powerful information. We combine data and context to paint the whole picture for our clients, around the clock - from around the world. In Data, we are responsible for delivering this data, news and analytics through innovative technology - quickly and accurately. We apply problem-solving skills to identify innovative workflow efficiencies, and we implement technology solutions to enhance our systems, products and processes. Our Team: The Ownership Data team is responsible for the data management of shareholding disclosures for institutional investors and insiders that fuels functions like HDS. In addition, the group maintains computed statistics like Equity Float and Insider Ownership for publicly listed companies. Overall, this aggregated, cleansed, and normalized data fuels analytics, supports News, and is heavily relied on by market players to understand market sentiment and drive investment decisions. The group is dedicated to crafting best-in-class ownership analytics and holdings data while constantly looking to enhance and expand our existing offerings. The Role: The Ownership Data team is seeking a driven and detail-oriented Ownership Quality Engineer to lead our data quality strategy and execute high-impact quality assurance initiatives across float and ownership data. You'll be responsible for identifying quality risks, designing workflows to detect and remediate issues, and working across teams to implement systematic improvements to our end-to-end data pipeline. As a Data Management Professional, you will help to develop our business outcome-based data strategies to optimize the value of data for our customers and improve data operations.

Requirements

A BA/BS degree or higher in Computer Science, Mathematics, Finance or relevant data technology field, or equivalent professional work experience
4+ years' experience in data analysis, financial market research, and/or information technology
Sound understanding of data quality as a domain of data management (DAMA CDMP, DCAM certification a plus)
Proven ability to conduct data profiling and data analysis (using Python is a plus) and visualize results
Experience working with and analyzing time-series data in python
Experience with anomaly detection techniques for both cross sectional and time-series data
Solid ability to combine technical data science traits with good business insight
Strong analytical abilities with passion for data and evidence-based decision-making

Nice To Haves

Working knowledge of shareholder filings and Equity Float and how they're used by financial markets players
Software development experience and knowledge of standard methodologies
Experience working with Big Data technologies such as Apache Spark for analytical workflows and data on a terabyte scale a plus

Responsibilities

Develop, refine, and implement the strategy for how to achieve best-in-class data quality for the Ownership and Float data products
Perform quarterly reports outlining data profiling for long, short, and float ownership data and apply statistical methods to support data quality measurements
Ensure the accuracy of our operations by reviewing outliers and optimizing the business rules used to identify them
Collaborate with subject matter authorities in Data, as well as colleagues in Product and Engineering, on roadmaps to improve data quality and ensure consistency in methodology
Lead projects globally to improve data quality for a variety of customer types and use cases
Employ quantitative methods to advise and enhance on decision making capability in business planning, process improvement, and solution management
Keep up with the industry trends, standards, and innovation in the data quality domain
Work in a fast paced, multifaceted & collaborative setting