Role Description 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. Qualifications - Bachelor's degree in Computer Science, Engineering, Information Systems, or related field; Master's degree preferred. - 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. Requirements - 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 - 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. Benefits - 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.