Director, Data Integration & Workflows

About this role

About the Role:

Grade Level (for internal use): 13 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 tuning—balancing 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 Succ

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