Head of Engineering – Inbound Data

S&P Global

. Lead and scale a world-class engineering organization of 100+ data engineers, ML engineers, and platform developers .

Posted 11/4/2026full-timeUnited StatesLead💰 $185,000 - $265,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive experience in leading large engineering teams and driving the development of enterprise-scale data platforms using Databricks and AWS. Capable of establishing engineering standards and fostering a high-performance culture focused on continuous learning and operational excellence.

Highest-signal resume keywords

Databricks Data Intelligence Platform ExpertiseAWS Architecture and Data Platform DesignLeadership in Scaling Engineering OrganizationsAutonomous AI Systems DeploymentEngineering Standards and Operational Excellence

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

DatabricksAWSApache IcebergTerraformCI/CD PipelinesSemantic ModelingData GovernanceRAG ArchitectureAutonomous AI Agent DeploymentMulti-Region Deployment Strategies

Soft Skills

LeadershipCommunicationInfluencing StakeholdersTalent DevelopmentPsychological Safety

Tools & Technologies

Amazon S3AWS GlueAmazon EMRAWS Lake FormationMLflowUnity CatalogDelta LakeDelta Live TablesAmazon SageMakerAmazon Bedrock

Certifications & Qualifications

AWS CertificationDatabricks Certification

Industry Keywords

Enterprise Data PlatformsData EngineeringMachine Learning EngineeringOperational SLAsIncident Management

Tech Stack

Tools & technologies

ApacheAWSCloudTerraformUnity

About the role

Key responsibilities & impact

Lead and scale a world-class engineering organization of 100+ data engineers, ML engineers, and platform developers
Manage managers and Staff/Principal-level engineers
Drive hiring, retention, and talent development strategies
Provide strategic technical direction for enterprise-scale lakehouse architecture using Databricks and AWS
Drive the transition to production-grade autonomous AI systems
Establish engineering standards and operational excellence practices, including SLOs, error budgets, incident management, and infrastructure-as-code
Oversee enterprise semantic modeling and RAG architecture strategies
Ensure AI systems use governed data assets with access controls, lineage tracking, and auditability
Foster a high-performance engineering culture centered on psychological safety, continuous learning, and rapid experimentation
Partner with Product, FinOps, and executive leadership to translate business goals into technical roadmaps

Requirements

What you’ll need

15+ years of engineering experience
5+ years directly managing and scaling large engineering teams at Director or Head of Engineering level
Experience managing managers and guiding Staff/Principal-level individual contributors through organizational transformation
Deep expertise in Databricks Data Intelligence Platform, including Unity Catalog, Delta Lake internals, Delta Live Tables, Databricks Workflows, MLflow, and Mosaic AI
Extensive experience architecting enterprise-scale data platforms on AWS, including Amazon S3, AWS Glue, Amazon EMR, and AWS Lake Formation
Strong understanding of multi-region deployment strategies and security patterns including IAM, VPC, and KMS
Technical depth in Apache Iceberg internals, schema evolution, and cross-platform table strategies
Hands-on experience in autonomous AI agent deployment and RAG pipeline architecture for production environments
Ability to build and scale engineering organizations from 20+ to 100+ engineers
Experience establishing engineering standards, agile delivery practices, and operational SLAs
Strong leadership and communication skills
Experience translating technical trade-offs to non-technical stakeholders and influencing executive decision-making through data-driven recommendations and ROI analysis
AWS and/or Databricks certifications preferred
Experience partnering with FinOps and cloud platform teams preferred
Experience with semantic modeling, knowledge graphs, and enterprise data governance frameworks preferred
Background in LLMOps, model evaluation, and prompt orchestration preferred
Familiarity with Amazon SageMaker and Amazon Bedrock preferred
Experience with Terraform, CI/CD pipelines, deployment automation, and Databricks Asset Bundles preferred

Benefits

Comp & perks

Annual incentive plan eligibility
Additional S&P Global benefits
Health care coverage
Generous time off
Continuous learning and career development resources
Competitive pay
Retirement planning
Continuing education program
Company-matched student loan contribution
Financial wellness programs
Family-friendly benefits and perks
Retail discounts
Referral incentive awards