Senior Data Management Professional - Data Automation Engineer - People Data

Bloomberg•Princeton, NJ

2d•$110,000 - $190,000

About The Position

The People Data team is seeking a highly motivated professional with an interest in data, operations, and technology to improve its data product by evolving data quality and acquisition systems. The role focuses on improving the quality, breadth, and scalability of People Data by transforming how data is acquired, onboarded, maintained, and validated through automation and AI-enabled workflows. The successful candidate will identify inefficient processes, design practical solutions, and drive implementation from concept through production. They will work across data, operations, and technology to build scalable systems that reduce manual effort, improve data quality, and accelerate coverage expansion. The People Data product provides information about corporate executives, directors, and other individuals who influence markets, supporting clients’ analysis of areas such as corporate governance, executive compensation, leadership changes, career history, and corporate diversity. The position is based in Princeton. The stated annual salary range is $110,000–$190,000, plus benefits and bonus. Actual compensation may vary based on geographic location, work experience, market conditions, education or training, and skill level.

Requirements

A BA/BS degree or higher in Computer Science, Mathematics, Information Systems, Finance, or a related field, or equivalent professional work 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 as part of 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.

Nice To Haves

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 using 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.

Responsibilities

Design and implement scalable solutions that improve data quality, automate data acquisition, and expand People Data product 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 to improve data quality, expand coverage, and scale data onboarding and 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 design and implement solutions for data quality and scalable 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 and empower colleagues on data quality, automation, and scalable data management principles.
Keep up with industry trends, standards, and innovation across data quality, data operations, AI, and engineering.
Work in a fast-paced, multifaceted, collaborative setting.