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
Conduct comprehensive code reviews for SME-built and team-developed pipelines, ensuring adherence to standards for maintainability, testing, logging, data validation, and documentation
Prepare IT-ready handover artifacts including technical documentation, test evidence, operational procedures, and clear support boundaries
Optimize pipeline performance and cost through appropriate partitioning strategies, caching, incremental processing patterns, and compute resource tuning.

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
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.
Must be fluent in English.

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.

Senior Analyst, Data Integration – Workflows

Key skills