Senior Technical Product Manager, Data Engineering & Data Science Solutions

S&P Global

Quick summary

Work type: On-site
Location: New York, NY · Boulder, CO · Princeton, NJ
Salary: $100,000–$149,000 / yr
Posted: 32 days ago
Closes: Mar 24, 2027

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $193k

This role $124k

$85k most similar roles pay here $242k

This role pays less than 96% of similar roles. Most pay $161,250–$223,875 — the shaded band above. At the midpoint, this role pays about $124k versus about $193k for comparable roles.

Based on 239 similar postings.

Employer

About S&P Global

S&P Global delivers Essential Intelligence® that shapes decision making. We provide the world’s leading organizations with the right data, connected technologies and expertise they need to move ahead.

S&P Global currently has 25 open roles on FindRole.

Listed pay typically runs $142,000–$207,700 across 15 roles with salary data.

Most-posted roles

View all roles at S&P Global

At a glance

TL;DR · Senior Technical Product Manager, Data Engineering & Data Science Solutions

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As a Senior Technical Product Manager in the Data Engineering & Data Science Solutions team at S&P Global Energy, you will drive the strategic direction and roadmap for critical data infrastructure components such as data onboarding, storage solutions, and core platform engines. Your day-to-day responsibilities include leading cross-functional teams to develop data engineering capabilities, admin utilities, and disaster recovery frameworks, while also championing MLOps practices and defining requirements for common data pipeline tools. You will need hands-on experience with advanced Databricks features like Delta Lake and MLflow, as well as expertise in Apache Spark, Kafka, Airflow, and cloud platforms such as AWS or Azure. This role requires a strong technical background and knowledge of SDLC methodologies, alongside the ability to translate business requirements into detailed product specifications for enterprise-scale analytics solutions within the financial services industry.

Skills

Databricks Delta Lake MLflow Apache Spark Kafka Airflow AWS Azure Google Cloud Platform MLOps Agile Scrum DevOps CI/CD

What you'll do

Drive product strategy and roadmap for critical data infrastructure components.
Lead cross-functional teams to deliver enterprise-scale analytics solutions.
Own product vision for disaster recovery frameworks ensuring platform reliability.
Define and execute ontology integration strategies to enhance semantic data capabilities.
Champion data quality initiatives, driving technical implementation of monitoring solutions.
Develop innersource ecosystem strategy enabling cross-team collaboration on core platforms.
Collaborate with engineering teams to implement MLOps practices for model lifecycle management.

What we're looking for

8+ years of product management experience in data platforms or analytics infrastructure.
Hands-on expertise with Databricks features like Delta Lake, MLflow, and Databricks SQL.
Strong background in data engineering technologies including Apache Spark, Kafka, Airflow.
Proven experience with cloud data platforms such as AWS, Azure, or Google Cloud Platform.
Understanding of MLOps processes for model deployment, monitoring, and lifecycle management.

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