Lead Data Analyst

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.
• Accountability for own work queues with proactive risk identification.
• Comfort working with repetitive tasks while maintaining accuracy.
• Proactive & growth mindset in improving the product and operations.
• Demonstrates strong learning agility and digital acumen, with a continuous learning mindset and the ability to adapt to evolving tools and technologies.

Preferred Education & Experience:

• Bachelor's degree in Finance, Commerce, Economics, Accounting, or related field from an accredited institution

• 0-3 years of experience in financial data operations, research, or related analytical roles (including internships and fresh graduates)

• Strong understanding of financial statements including Balance Sheet, Income Statement, and Cash Flow statements and their interrelationships

• Proficiency in MS Office applications, particularly Excel for data manipulation, analysis, and basic reporting functions

• Experience with database tools such as SQL for querying, filtering, and reconciling financial data within established guidelines

• Excellent attention to detail with ability to follow SOPs precisely and maintain data quality standards consistently

Additional Preferred Qualifications:

• Knowledge of US GAAP or international financial reporting standards and their application in data standardization processes

• Familiarity with financial data platforms such as Bloomberg, Reuters, or similar market data systems

• Experience with automation tools including Python programming basics or advanced Excel functions such as macros for process efficiency

• Understanding of global financial markets and public company reporting requirements across different jurisdictions

Key Interfaces & Stakeholders:
• Data Research & Operations team members (Associates, Specialists)
• Data Quality and Data Stewards teams for issue assessment and resolution
• Process Leads / Front-Line Managers and internal support teams (via workflow or ticketing tools)