Lead Data Analyst

Grade Level (for internal use): 08 Job Description Role Scope: The Data Research Associate is an individual contributor role that develops foundational proficiency in one or more workflows or datasets. The role supports end-to-end data lifecycle activities including sourcing, basic validation, enrichment, and delivery under guidance, adhering strictly to standard operating procedures (SOPs) and set guidelines, and escalating deviations for a defined dataset or workflow. The role emphasizes building strong data literacy and operational discipline, learning industry concepts, and collaborating with other data research teams. Associates contribute to UAT and day-to-day solutioning, follow established standards, and participate in continuous process improvement and the adoption of technology and AI-enabled workflows. What You’ll Do (Key Responsibilities): Operational Delivery: Execute day-to-day data collection, extraction, and basic cleansing for Compustat Fundamentals datasets from structured and unstructured sources including regulatory filings, earnings reports, and vendor feeds Follow established SOPs for data sourcing, tagging, and loading while ensuring required metadata and quality checks are completed accurately and within defined timelines Perform first-level data validation checks to ensure completeness and accuracy of financial records by applying predefined business rules, duplicate checks, and reconciliations Support the integrity of global financial datasets covering public companies by maintaining accurate documentation and enabling traceability through workflow tools Collaborate with team members and escalate exceptions or anomalies promptly with evidence and recommended solutions to ensure data quality standards are met Contribute to process improvement initiatives by identifying minor operational pain points and suggesting incremental enhancements for senior review Stakeholder Management • Coordinate with immediate team members to clarify requirements and resolve day-to-day data issues. • Share timely updates with senior team members on production volumes, issues, and delays. • Participate in team stand-ups, huddles, and calibration sessions to align priorities, quality expectations, and standards. How Impact Will Be Measured (Success Metrics): Percentage of tasks completed within defined SLAs for volume and timeliness. • First-pass data quality outcomes (accuracy and completeness) and reduction in repeat errors. • Adherence to SOPs and documentation standards, reflected in clean audits and zero critical deviations. • Responsiveness to feedback and learning velocity, including effective queue management during peak periods. • Handover/documentation completeness at 100% for assigned queues (steps, sources, checks, evidence). For This Role You'll Need to Have (Skills & Competencies): Technical Skills • Familiarity with structured/unstructured data formats (Excel, PDFs, filings, news, vendor feeds, etc). • Beginner knowledge of SQL/Excel for querying, filtering, and reconciling data. • Awareness of automation and GenAI tools; ability to follow prescribed usage patterns. • Exposure to data lifecycle concepts (source → ingest → validate → publish).   Functional Skills • Basic understanding of financial/market or domain data concepts relevant to the assigned dataset. • Ability to perform data entry, tagging, and basic enrichment accurately. • Capability to read and interpret simple source materials (e.g., filings, vendor feeds, news). • Structured escalation of exceptions with appropriate evidence and context. • Ability to quickly learn and work with enterprise data capture tools, workflow systems, and ticketing platforms.   Behavioral Skills • Strong attention to detail and operational discipline; balance speed with accuracy. • Learning agility and openness to feedback and coaching. • Clear communication and collaboration during team interaction. •