About the Role:

Grade Level (for internal use):

The Team:

The Director of Data Integration & Workflows leads the team that serves as the backbone of enablement at S&P Dow Jones Indices (SPDJI) responsible for building templating and standardizing the data pipelines and workflow patterns that power our index and analytical solutions. Reporting to the Director of Data AI & Enablement this role is critical to the Data Platform enablement strategy accelerating delivery reducing production risk and ensuring that every pipeline and workflow is scalable observable and maintainable.

Responsibilities and Impact:

Strategic Leadership & Technical Vision

• Define and drive the data automation and transformation strategy for the Data Integration & Workflows group setting standards and approaches for building production-ready pipelines and workflow automation across value streams
• Establish reference architectures and engineering standards for ETL/ELT orchestration error handling observability and performance/cost optimization with clear definition of done criteria for production readiness
• Collaborate with the PPD Group to build sustainable transformational enhancements to the data platform and associated tools
• Foster a culture of technical excellence craftsmanship reusable component development and continuous improvement in automation maturity
• Contribute to the broader Data AI & Enablement strategy ensuring Data Integration & Workflows capabilities align with organizational strategic goals

Delivery Through Enablement

• Lead delivery through enablement by assessing SME technical capability and selecting the right engagement model
• Partner with value stream SMEs to co-develop and review pipelines adapting support based on SME technical capability and fostering their growth
• Oversee the design and implementation of robust reusable data integration and workflow patterns for both batch and streaming use cases
• Partner with PPD on feasibility and planning providing realistic estimates identifying dependencies and shaping technical scope to ensure delivery commitments are achievable and measurable
• Develop and implement training programs to enhance the technical proficiency of value stream SMEs in data engineering practices

Quality Assurance & Production Readiness

• Run the code review and quality gate process for SME-built pipelines ensuring consistency in maintainability testing logging data validation and documentation prior to IT handover
• Ensure all solutions are production-ready with comprehensive documentation testing error handling and operational monitoring
• Coordinate seamless IT handover and production gateway readiness ensuring complete deployment packages (runbooks architecture notes testing evidence monitoring/alerting expectations)
• Partner with IT during QA to resolve issues quickly and ensure solutions meet enterprise standards for quality security and supportability
• Implement and maintain governance frameworks specific to data integration ensuring compliance with organizational policies and industry standards

Operational Excellence

• Provide L3 support for production business-logic issues (in collaboration with value stream SMEs) driving root-cause analysis and permanent fixes for recurring pipeline failures or data breaks
• Ensure implementation of strong data reliability controls including validation rules reconciliation checks anomaly detection and completeness/timeliness controls that protect downstream index processes
• Drive performance and cost optimization through appropriate partitioning caching incremental processing patterns and compute usage tuningbalancing speed stability and platform spend
• Establish and monitor operational metrics to track solution delivery timeliness pipeline reliability and platform performance

Team Development & Collaboration

• Lead mentor and develop a high-performing team of Data Integration Leads and Experts
• Build data engineering capability across the organization through structured mentorship knowledge sharing and hands-on coaching
• Collaborate effectively with the other pillars within Data AI & Enablement (AI Solutions and Data Governance) to ensure cohesive platform enablement
• Foster strong partnerships with PPD teams IT Data Value Streams and Data Services & Strategy to align technical enablement efforts with business priorities

Shared Accountabilities

• With PPD:Collaborate on prioritization and alignment of data integration efforts with business requirements and strategic goals; provide realistic technical assessments to inform planning.
• With IT:Partner to ensure infrastructure readiness smooth deployment of production-ready solutions and operational excellence; coordinate handover processes and support production gateway requirements.
• With Data Value Streams:Engage with value stream SMEs to co-develop solutions ensuring alignment with business logic and domain expertise; assess and develop SME technical capabilities.
• With Data Services & Strategy:Work together with Vendor Governance and Catalog teams to establish scalable data integration patterns and ensure proper metadata and lineage tracking.
• With AI Solutions & Data Governance Teams:Coordinate on cross-cutting concerns including data quality standards AI data pipeline requirements and governance compliance.

Ownership

• Data Integration & Workflow Strategy:Own the technical strategy standards and execution approach for all data pipeline and workflow automation initiatives
• Production Readiness Framework:Responsible for defining and enforcing the definition of done for production-ready data solutions
• SME Enablement in Data Engineering:Own the capability development strategy for value stream SMEs in data integration and automation
• Quality Gates & Code Review Process:Maintain the quality assurance framework for all data pipelines prior to production deployment

What Success Looks Like

• Accelerate Solution Delivery:Streamline data pipeline development and deployment processes to deliver production-ready solutions efficiently and on schedule
• Enhance SME Technical Capabilities:Develop and implement comprehensive training and enablement programs to increase technical proficiency of value stream SMEs in data engineering
• Ensure Production Quality:Establish and maintain robust quality standards ensuring all pipelines are reliable maintainable observable and cost-effective
• Drive Reusability & Standardization:Build a library of reusable components templates and patterns that accelerate consistent delivery across domains

Key Performance Indicators (KPIs)

• Solution Delivery Timeliness:Percentage of production-ready data pipelines delivered on schedule
• SME Capability Improvement:Measurable improvement in technical skills of value stream SMEs through evaluations certifications and feedback
• Reusability Adoption:Adoption rate of standardized components and templates across value streams
• Platform Cost Optimization:Demonstrated improvements in pipeline performance and cost efficiency
• Manage and maintain key relationships with other groups within the department and across other departments particularly Product Management Client Coverage Index Services and Technology Teams.

Compensation/Benefits Information: (This section is only applicable to US candidates)

S&P Global states that the anticipated base salary range for this position is $149031 to $228996. Final base salary for this role will be based on the individuals geographic location as well as experience level skill set training licenses and certifications.

In addition to base compensation this role is eligible for an annual incentive plan.

This role is eligible to receive additional S&P Global benefits.

What Were Looking For:

Basic Required Qualifications:

Education & Experience

• Bachelors degree in Computer Science Engineering Information Systems or related field; Masters degree preferred
• 10 years of experience in data engineering data integration or related technical roles
• 5 years of leadership experience managing technical teams and delivering complex data solutions
• Proven track record of building and scaling production data platforms in complex entertrise environments
• Experience in financial services or index management preferred

Technical Expertise

• Deep expertise in ETL/ELT design patterns data pipeline architecture and workflow orchestration framework
• Strong knowledge of both batch and streaming data processing technologies
• Experience with modern data platforms cloud infrastructure (AWS Azure or GCP) and data engineering tools
• Proficiency in programming languages commonly used in data engineering (Python SQL Scala etc.)
• Understanding of data quality frameworks observability monitoring and alerting systems
• Knowledge of DevOps practices CI/CD pipelines and infrastructure-as-code
• Familiarity with data governance principles and compliance frameworks

Leadership & Soft Skills

• Strong leadership and team management skills with ability to inspire mentor and develop technical talent
• Excellent collaboration skills with ability to partner effectively across technical and business teams
• Outstanding communication abilities able to articulate complex technical concepts to non-technical audiences and translate business requirements into technical solutions
• Strategic thinking with ability to align team activities with business objectives and drive innovation
• Problem-solving mindset with focus on root-cause analysis and sustainable solutions
• Experience managing stakeholder expectations and delivering on commitments in a matrixed organization

Preferred Qualifications:

• Experience across the breadth of index concepts and asset classes
• Familiarity with SPDJI products methodologies or index calculation processes
• Expertise in Python or SQL Development
• Experience with Agile/Scrum methodologies and product-oriented delivery models
• Knowledge of data lineage metadata management and data cataloging

Right to Work Requirements:

This role is limited to persons with indefinite right to work in the United States.

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.