Sr. Director, Software Engineering - Data Platform & MDM Integration

About the role

Grade Level (for internal use): 14 The Team: This team is essential to the division's data strategy, playing a key role in data transformation and leading efforts to replace legacy technical debt. The team embodies a strong commitment to delivering on business needs and demonstrates exceptional collaboration with both product and business partners, bringing high energy and dedication to their work while being known for their collaborative approach and strong technical expertise. Responsibilities and Impact: Lead strategic MDM integration initiatives - Architect and oversee seamless, high-volume data integration between Databricks lakehouse and NeoXam MDM platform, establishing foundational data architecture for multi-billion-dollar index-linked investment products Drive advanced cloud data architecture - Utilize cutting-edge Databricks features including Unity Catalog, Delta Live Tables, and Serverless compute to optimize data pipelines and maximize performance for massive-scale financial datasets Pioneer AI/ML automation solutions - Implement Databricks Mosaic AI and agentic frameworks to automate data transformation, quality assurance, and anomaly detection across the entire conversion lifecycle Build and scale high-performing engineering teams - Lead, manage, and grow multiple teams of product-focused software and data engineers while mentoring their professional and technical development Modernize legacy infrastructure - Spearhead mission-critical migration of legacy assets and terabytes of data to modern AWS cloud ecosystem, breaking monolithic applications into microservice architectures Champion engineering excellence and innovation - Foster a culture of cutting-edge technology adoption, continuous improvement of software engineering practices, and delivery of scalable, resilient solutions 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 $185,000 to $265,000. Final base salary for this role will be based on the individual's 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 not eligible for additional compensation such as an annual incentive bonus or sales commission plan. This role is eligible to receive additional S&P Global benefits. For more information on the benefits we provide to our employees, please click here .

Requirements

• Basic Required Qualifications:
• Bachelor's degree in Computer Science, Engineering, or related technical field
• Minimum 9 years of experience in software engineering and data platform development with proven track record in enterprise-scale solutions
• Minimum 7 years of people management experience leading technical teams and driving organizational growth in fast-paced environments
• Advanced expertise in cloud data platforms such as Databricks, Snowflake, or similar, with hands-on experience in data lakehouse architectures and ETL/ELT processes
• Strong experience with cloud infrastructure including AWS services (such as S3, EMR, Glue, Lambda) or equivalent cloud platforms (Azure, GCP)
• Proven leadership in large-scale data migrations and system modernization initiatives, with experience managing complex technical transformations
• Additional Preferred Qualifications:
• Master's degree in Computer Science, Software Engineering, or related technical discipline with 13+ years of progressive software development and data architecture experience
• Deep expertise in Master Data Management platforms such as NeoXam, Informatica MDM, or IBM InfoSphere, with proven success integrating enterprise data platforms
• Advanced AI/ML and Generative AI experience including hands-on work with machine learning frameworks (such as MLflow, Kubeflow, or SageMaker) and LLM implementation using tools like LangChain, Hugging Face, or similar platforms
• Extensive experience with Big Data technologies including distributed computing frameworks (such as Apache Spark, Hadoop, or Flink) and modern data processing architectures
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