Lead Data Analyst

Role Overview

The Lead Data Analyst will be responsible for sourcing, validating, and maintaining critical reference and market data used in index construction and portfolio management. The role will also involve working on private markets data and content, supporting the development of new indexes, operational processes, and index review activities.

What You Will Do

The responsibilities of the Lead Data Analyst will include owning the business definition, scope, quality, sourcing strategy, operating model, and lifecycle of private-markets data used by Morningstar Indexes. This will involve defining and maintaining the private-markets data taxonomy, business definitions, critical data elements, entity relationships, source hierarchy, curation rules, and coverage standards.

Why It Might Be a Fit

The ideal candidate will have at least 5 years of relevant experience in private-markets data, financial data operations, investment research, data products, index operations, asset management, or a financial data provider. They will also have strong knowledge of private markets entities, funds, ownership structures, and valuations, as well as experience defining data-quality standards, validation rules, exception processes, and operational controls.

Requirements

• At least 5 years of relevant experience in private-markets data, financial data operations, investment research, data products, index operations, asset management, or a financial data provider.
• Demonstrated ownership of a dataset, data-content capability, data operations process, or business data product.
• Strong knowledge of private markets entities, funds, ownership structures, and valuations
• Experience defining data-quality standards, validation rules, exception processes, and operational controls.
• Experience designing or improving data-sourcing, collection, curation, and maintenance processes.
• Experience evaluating and managing external data sources or data vendors.
• Ability to develop and prioritize a business data roadmap and translate stakeholder needs into requirements and acceptance criteria.
• Working knowledge of SQL sufficient for data investigation, reconciliation, validation, and UAT.
• Working knowledge of Python sufficient to implement simple ETL pipelines.
• Strong analytical, problem-solving, prioritization, and decision-making capabilities.
• Strong stakeholder-management and influencing skills, including the ability to work effectively with research, operations, product, technology, procurement, and vendors.
• Strong written communication and documentation skills.

Benefits

• Benefits package
• Flexible work environment
• Opportunity to collaborate in-person each week

Tagged as: Data Analysis, Pipeline Management, Python, SQL