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 expertise in leading large engineering teams and architecting enterprise-scale data platforms, particularly utilizing Databricks and AWS technologies. Proven ability to establish engineering standards and foster a high-performance culture while translating business goals into technical strategies.

Highest-signal resume keywords

Databricks Data Intelligence PlatformAWS Cloud ServicesEngineering Organization ScalingLeadership and Communication SkillsData Governance Frameworks

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Enterprise-Scale Data ArchitectureAutonomous AI Systems DeploymentApache Iceberg InternalsSemantic ModelingRAG Pipeline ArchitectureInfrastructure-as-CodeAgile Delivery PracticesOperational SLAsMulti-Region Deployment StrategiesError Budgets

Soft Skills

LeadershipCommunicationPsychological SafetyContinuous LearningInfluencing Decision-Making

Tools & Technologies

Databricks WorkflowsAWS GlueAmazon S3Amazon EMRTerraformCI/CD PipelinesAmazon SageMakerAmazon BedrockDelta Lake InternalsMLflow

Certifications & Qualifications

AWS CertificationDatabricks Certification

Industry Keywords

Data EngineeringAI SystemsData GovernanceKnowledge GraphsOperational Excellence

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 Data Intelligence Platform and AWS cloud services
Drive transition to production-grade autonomous AI systems using the Databricks AI stack
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 rooted in 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
Proven 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
Demonstrated ability to build and scale engineering organizations from 20+ to 100+ engineers
Experience establishing rigorous engineering standards, agile delivery practices, and operational SLAs
Strong leadership and communication skills with experience translating complex technical trade-offs to non-technical stakeholders
Experience 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
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 benefits and perks
Retail discounts
Referral incentive awards