About the Role:

Grade Level (for internal use):

11

The Team:

The Senior Analyst, Data Integration & Workflows plays a critical role within the Data AI & Enablement organization, serving as a senior technical leader responsible for designing, implementing, and operationalizing production-grade data pipelines and workflow automation that power SPDJI's index and analytical solutions. This role combines hands-on technical expertise with leadership capabilities to drive delivery excellence, mentor technical talent, and ensure all data integration solutions meet enterprise standards for quality, reliability, and maintainability.

Responsibilities and Impact:

Technical Leadership & Solution Delivery

• Lead complex data integration initiatives from design through production deployment, ensuring solutions are scalable, observable, and aligned with enterprise architecture standards
• Design and implement production-grade data pipelines (batch and streaming) that transform raw inputs into trusted curated outputs, incorporating robust error handling, validation, and reconciliation controls
• Establish and evangelize engineering best practices for ETL/ELT patterns, workflow orchestration, data quality controls, and operational observability across the team and value streams
• Drive technical decision-making for pipeline architecture, technology selection, and design patterns, balancing business requirements with technical feasibility and long-term maintainability
• Partner with PPD on technical planning and feasibility, providing realistic estimates, identifying technical dependencies, and shaping scope to ensure achievable delivery commitments

Enablement & Co-Development

• Lead hands-on enablement with value stream SMEs through pair programming, structured guidance, and co-development sessions—adapting approach based on SME technical capability
• Assess SME technical readiness and recommend appropriate engagement models (SME-led with review, co-development, or led build with validation)
• Build reusable automation components and templates (frameworks for ingestion, validation, transformation, publishing, backfills) that accelerate consistent delivery across domains
• Develop SME technical capabilities through targeted coaching, code reviews, and knowledge transfer, fostering a culture of engineering excellence and continuous learning
• Create and maintain technical documentation, including reference architectures, design patterns, coding standards, and implementation guides

Quality Assurance & Production Readiness

• Conduct comprehensive code reviews for SME-built and team-developed pipelines, ensuring adherence to standards for maintainability, testing, logging, data validation, and documentation
• Implement data reliability controls including validation rules, reconciliation checks, anomaly detection, and completeness/timeliness monitoring that protect downstream index processes
• Engineer observability and monitoring solutions by implementing logging standards, metrics, alerts, and runbooks that enable effective production support
• Prepare IT-ready handover artifacts including technical documentation, test evidence, operational procedures, and clear support boundaries
• Partner with IT during QA and deployment, resolving issues quickly and ensuring solutions meet enterprise standards for security, supportability, and operational excellence

Operational Excellence & Continuous Improvement

• Provide L3 support for production business-logic issues, collaborating with value stream SMEs to drive root-cause analysis and implement permanent fixes for recurring failures
• Optimize pipeline performance and cost through appropriate partitioning strategies, caching, incremental processing patterns, and compute resource tuning
• Implement workflow orchestration patterns (scheduling, dependency management, retries, idempotency, parameterization) ensuring pipelines are resilient to upstream variability
• Capture and share lessons learned, updating engineering playbooks, patterns, and standards based on production outcomes and emerging best practices
• Monitor operational metrics related to pipeline reliability, data quality, performance, and cost efficiency; drive continuous improvement initiatives

Collaboration & Stakeholder Management

• Collaborate with Data Integration Lead to shape team strategy, prioritize initiatives, and align technical approaches with organizational goals
• Partner effectively with AI Solutions and Data Governance teams on cross-cutting concerns including data quality standards, AI pipeline requirements, and compliance
• Engage with Data Value Streams to understand business requirements, validate technical solutions, and ensure alignment with domain expertise
• Work with Data Services & Strategy teams (Vendor Governance, Catalog) to establish scalable integration patterns and ensure proper metadata and lineage tracking
• Build strong relationships with IT and PPD teams to ensure infrastructure readiness, smooth deployments, and operational excellence

Shared Accountabilities

• With Data Integration Lead: Execute on team strategy; provide technical leadership on complex initiatives; mentor junior team members; contribute to standards and capability development
• With PPD: Collaborate on technical feasibility assessments and planning; provide realistic estimates; align integration efforts with platform capabilities and roadmap
• With IT: Ensure infrastructure readiness; coordinate handover processes; support production gateway requirements; partner on operational excellence
• With Data Value Streams: Co-develop solutions with SMEs; validate business logic alignment; assess and develop SME technical capabilities
• With Data Services & Strategy: Establish scalable integration patterns; ensure proper metadata and lineage tracking; align with vendor governance requirements
• With AI Solutions & Data Governance: Coordinate on data quality standards, AI data pipeline requirements, and governance compliance

Ownership

• Complex Technical Initiatives: Own the end-to-end delivery of high-complexity data integration and workflow automation projects
• Engineering Standards Implementation: Responsible for implementing and enforcing technical standards, patterns, and best practices within assigned domain or value streams
• SME Technical Development: Own the hands-on enablement and capability development of assigned value stream SMEs in data engineering practices
• Production Solution Quality: Accountable for ensuring all solutions meet production readiness criteria before IT handover

Parameters for Success

• Deliver Production-Ready Solutions: Consistently deliver high-quality, production-ready data pipelines that meet business requirements and enterprise standards
• Build SME Capability: Demonstrably improve technical capabilities of value stream SMEs through effective enablement and mentorship
• Drive Reusability: Create and promote adoption of reusable components and standardized patterns that accelerate delivery
• Ensure Operational Excellence: Implement robust observability, monitoring, and support frameworks that minimize production incidents and enable rapid issue resolution
• Foster Technical Excellence: Contribute to a culture of craftsmanship, continuous improvement, and engineering best practices

Key Performance Indicators (KPIs)

• Solution Delivery Quality: Percentage of delivered pipelines that pass IT QA on first submission; production incident rate for delivered solutions
• SME Capability Development: Measurable improvement in technical skills of mentored SMEs through assessments, code review quality progression, and feedback
• Operational Reliability: Pipeline uptime and reliability metrics; mean time to resolution for production issues; data quality incident rates

What We’re Looking For:

Basic Required Qualifications:

Experience

• 8+ years of experience in data engineering, data integration, or related technical roles with progressive responsibility
• 3+ years of experience in technical leadership roles, including mentoring engineers and leading complex technical initiatives
• Proven track record of designing and implementing production data pipelines in complex enterprise environments
• Experience in financial services, index management, or similar data-intensive industries preferred

Technical Expertise

• Proficiency in ETL/ELT design patterns, data pipeline architecture, and workflow orchestration frameworks
• Advanced programming skills in languages commonly used in data engineering (Python, SQL, Scala, or similar)
• Solid understanding of data quality frameworks, data validation techniques, reconciliation patterns, and anomaly detection
• Experience implementing observability, monitoring, and alerting systems for production data pipelines
• Familiarity with data governance principles, metadata management, and compliance frameworks

Leadership & Soft Skills

• Strong technical leadership with demonstrated ability to lead complex initiatives and influence technical direction
• Excellent mentorship and coaching abilities, with track record of developing technical talent and improving team capabilities
• Outstanding collaboration skills with ability to partner effectively across technical and business teams in a matrixed organization
• Clear communication abilities, able to articulate complex technical concepts to varied audiences and translate business requirements into technical solutions
• Problem-solving mindset with focus on root-cause analysis, sustainable solutions, and continuous improvement
• Adaptability and pragmatism in selecting appropriate engagement models based on SME capability and project requirements
• Strong stakeholder management skills with ability to manage expectations and deliver on commitments

Preferred Qualifications:

• Bachelor's degree in Computer Science, Engineering, Information Systems, or related field; Master's degree preferred
• Experience with index calculation processes, financial data workflows, or SPDJI products and methodologies
• Certification in Python or SQL Development (e.g., Python Institute, Microsoft SQL certifications)
• Experience with Agile/Scrum methodologies and product-oriented delivery models
• Knowledge of data lineage, metadata management, and data cataloging tools (e.g., Collibra, Alation, DataHub)
• Experience with AI/ML data pipeline requirements and integration patterns

We require all external candidates who reach the final stage of our interview process to attend at least one in-person interview, which is ordinarily at your nearest S&P Global office. This must be completed before we can proceed to an offer.

What’s In It For You?

Our Mission:

Advancing Essential Intelligence.

Our People:

We're more than 35,000 strong worldwide—so we're able to understand nuances while having a broad perspective. Our team is driven by curiosity and a shared belief that Essential Intelligence can help build a more prosperous future for us all.From finding new ways to measure sustainability to analyzing energy transition across the supply chain to building workflow solutions that make it easy to tap into insight and apply it. We are changing the way people see things and empowering them to make an impact on the world we live in. We’re committed to a more equitable future and to helping our customers find new, sustainable ways of doing business. Join us and help create the critical insights that truly make a difference.

Our Values:

Integrity, Discovery, Partnership

Throughout our history, the world's leading organizations have relied on us for the Essential Intelligence they need to make confident decisions about the road ahead. We start with a foundation of integrity in all we do, bring a spirit of discovery to our work, and collaborate in close partnership with each other and our customers to achieve shared goals.

Benefits:

We take care of you, so you can take care of business. We care about our people. That’s why we provide everything you—and your career—need to thrive at S&P Global.

Our benefits include: 

• Health & Wellness: Health care coverage designed for the mind and body.

• Flexible Downtime: Generous time off helps keep you energized for your time on.

• Continuous Learning: Access a wealth of resources to grow your career and learn valuable new skills.

• Invest in Your Future: Secure your financial future through competitive pay, retirement planning, a continuing education program with a company-matched student loan contribution, and financial wellness programs.

• Family Friendly Perks: It’s not just about you. S&P Global has perks for your partners and little ones, too, with some best-in class benefits for families.

• Beyond the Basics: From retail discounts to referral incentive awards—small perks can make a big difference.

For more information on benefits by country visit: https://spgbenefits.com/benefit-summaries

Global Hiring and Opportunity at S&P Global:

At S&P Global, we are committed to fostering a connected and engaged workplace where all individuals have access to opportunities based on their skills, experience, and contributions. Our hiring practices emphasize fairness, transparency, and merit, ensuring that we attract and retain top talent. By valuing different perspectives and promoting a culture of respect and collaboration, we drive innovation and power global markets.

Recruitment Fraud Alert:

If you receive an email from a spglobalind.com domain or any other regionally based domains, it is a scam and should be reported to reportfraud@spglobal.com. S&P Global never requires any candidate to pay money for job applications, interviews, offer letters, “pre-employment training” or for equipment/delivery of equipment. Stay informed and protect yourself from recruitment fraud by reviewing our guidelines, fraudulent domains, and how to report suspicious activity here.

20 - Professional (EEO-2 Job Categories-United States of America), ANLYTC202.2 - Middle Professional Tier II (EEO Job Group), SWP Priority – Ratings - (Strategic Workforce Planning)