S&P Global Off Campus Drive 2026 – Data Analyst

Basic Qualification:

• Fresher (2025/2026 passouts) – Bachelor’s/Masters degree in Finance, Economics, Commerce.
• Excellent in written & verbal communication
• Willing to work in rotational shift (including night shift) basis the business requirement
• Basic understanding of financial/market or domain data concepts relevant to the assigned dataset.

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:

• 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.

Key Responsibilities:

Operational Delivery

• Collect, extract, and cleanse data for assigned workflows or datasets from structured and unstructured sources.
• Execute sourcing, tagging, loading, and basic enrichment activities in strict adherence to SOPs, rule books, and predefined
• validation checks.
• Execute day-to-day data collection, extraction, and basic cleansing using approved manual and AI-assisted methodologies.
• Record production accurately in workflow and queue management tools, ensuring clear notes for traceability and handovers.
• Monitor assigned queues and proactively flag delays, blockers, data gaps, or unusual patterns for senior team members.
• Operate within approved rules and workflows; pause processing and escalate promptly when inputs deviate, or sources are
• unclear.
• Meet defined daily and weekly production and quality targets for assigned queues.
• Provide clear logs and supporting evidence to enable basic root-cause analysis when issues occur.

Process Excellence:

• Maintain audit-ready documentation and follow established handover protocols and controls.
• Adhere consistently to governance standards, controls, and documentation requirements.
• Apply quality feedback consistently to reduce repeat errors and follow the first-time correct approach.
• Suggest small, incremental process improvements (e.g., checklists, prompts, query snippets) for senior review and approval.
• Participate in automation- and AI-assisted workflows and small pilots under governance, using approved tools and prompts to support efficiency, exception handling, and quality improvement.

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.

Role

• The Data Analyst 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.