Senior Data Management Professional - Data Automation Engineer - People Data

USD 110,000-190,000 per year

SENIOR

✅ On-site

Tech Stack
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3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

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AI @ 4 Agile @ 4 Communication @ 7 Data Analysis @ 4 Data Engineering @ 4 Data Pipelines @ 4 Machine Learning @ 4 Mathematics @ 4 NLP @ 4 Profiling Project Management @ 7 Python @ 7 Tableau @ 4

Details

Bloomberg's Data teams deliver data, news, and analytics through innovative technology. The People Data team provides datasets on corporate executives, directors, and other individuals to help clients identify risks and opportunities through analysis of corporate governance, executive compensation, leadership changes, career history, and corporate diversity.

The role focuses on improving the quality, breadth, and scalability of the People Data product by transforming data acquisition, onboarding, maintenance, and validation through automation and AI-enabled workflows. The position works across data, operations, and technology to build scalable systems that reduce manual effort, improve data quality, and accelerate coverage expansion.

Responsibilities

Design and implement scalable solutions to improve data quality, automate data acquisition, and expand People Data coverage.
Identify recurring manual processes, operational bottlenecks, and quality failure points, and translate them into automation opportunities and preventive controls.
Build and deploy automation solutions for data quality, data onboarding, and data maintenance across datasets.
Investigate complex operational challenges and develop scalable solutions that reduce manual effort and improve data reliability.
Perform data profiling and apply analytical methods to support data quality measurements, rulesets, and monitoring frameworks.
Collaborate with domain experts in Data and colleagues in Product, Enterprise, and Engineering to improve data quality and scale acquisition systems.
Design and implement AI-enabled workflows to classify, extract, enrich, reconcile, validate, and prioritize People Data at scale.
Define success metrics for automation impact, data quality improvement, operational efficiency, and coverage expansion.
Educate colleagues on data quality, automation, and scalable data management principles.
Keep up with trends, standards, and innovation in data quality, data operations, AI, and engineering.

Requirements

BA/BS degree or higher in Computer Science, Mathematics, Information Systems, Finance, or a related field, or equivalent professional experience.
At least 3 years of professional experience in Data Quality Management, Data Management, Data Operations, Data Acquisition, Data Governance, or related disciplines within the Finance or Technology industries.
Experience applying AI, machine learning, NLP, or rules-based automation to data acquisition, enrichment, validation, or operational workflows.
Experience developing data quality metrics and business rules within a broader data architecture or operational framework.
Understanding of data pipelines, workflow orchestration, and automation frameworks used to acquire, transform, and maintain data at scale.
Experience identifying workflow inefficiencies and partnering with technical teams to deliver scalable data solutions.
Demonstrated experience designing and implementing automation solutions that eliminate manual workflows and improve operational scalability.
Experience writing production-ready code and conducting unit and/or integration testing.
Strong Python skills, including experience building production-grade data workflows, automation solutions, and analytical tooling.
Understanding of data engineering best practices.
Strong communication and project management skills.
Ability to combine technical skills with business insight.

Preferred Qualifications

DAMA CDMP or DCAM certification.
Experience designing human-in-the-loop workflows for data review, exception handling, or quality control.
Agile/Scrum project management experience.
Experience with data analysis and visualization tools such as Tableau or QlikSense.
Experience with financial data, people data, company data, private markets data, corporate governance data, or executive and board datasets.

Benefits

Benefits may include merit increases, incentive compensation for exempt roles, paid holidays, paid time off, medical, dental, vision, short- and long-term disability benefits, 401(k) matching, life insurance, and wellness programs.