> Markdown version of [/jobs/ext/1796676-head-of-engineering-inbound-data](https://www.wearedevelopers.com/jobs/ext/1796676-head-of-engineering-inbound-data). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Head of Engineering - Inbound Data - **Company:** Sp Global, Inc. - **Location:** Princeton, NJ, United States - **Experience:** Expert - **Salary:** $185,000.0 - $265,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Amazon S3, Apache HTTP Server, Audit Trail, Continuous Integration, Data Governance, Data Transformation, Data Security, Graph Database, Identity and Access Management, Meta-Data Management, Azure Machine Learning, Search Technologies, Cloud Platform System, Model Validation, Technical Debt, Amazon Virtual Private Cloud (VPC), Data Strategy, Data Lakes, AI Platforms, Infrastructure Automation Frameworks, Data Lineage, Deployment Automation, AWS Glue, Data Analytics, Data Management, Machine Learning Operations, Virtual Agents, Terraform, Amazon Elastic Mapreduce (EMR), Databricks - **Published:** July 28, 2026 - **Apply:** https://www.juju.com/job/00000000gk3d6j ## About the Role + 15+ years of engineering experience with 5+ years directly managing and scaling large engineering teams at Director or Head of Engineering level, including 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, with ability to engage in technical discussions and provide strategic guidance on complex platform trade-offs + Extensive experience architecting enterprise-scale data platforms on AWS including services such as Amazon S3, AWS Glue, Amazon EMR, AWS Lake Formation, with strong understanding of multi-region deployment strategies and security patterns (IAM, VPC, KMS) + Proven technical depth in Apache Iceberg internals, schema evolution, and cross-platform table strategies, combined with 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 while establishing rigorous engineering standards, agile delivery practices, and operational SLAs for complex data and AI products + Strong leadership and communication skills with experience translating complex technical trade-offs to non-technical stakeholders and influencing executive decision-making through data-driven recommendations and ROI analysis Additional Preferred Qualifications: + AWS and/or Databricks certifications with experience partnering with FinOps and cloud platform teams to optimize cloud consumption, performance, and platform ROI across multi-cloud data platform strategies + Experience with semantic modeling, knowledge graphs, and enterprise data governance frameworks including metadata management and secure data sharing patterns at enterprise scale + Background in LLMOps, model evaluation, and prompt orchestration with familiarity integrating AWS AI/ML services such as Amazon SageMaker and Amazon Bedrock into broader enterprise AI platform strategies + Experience with infrastructure-as-code tools such as Terraform, CI/CD pipelines, and deployment automation including Databricks Asset Bundles for repeatable, auditable platform delivery across distributed cloud environments ## Description 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. They are known for their collaborative approach, strong technical expertise, and the high energy and dedication they bring to their work. Responsibilities and Impact: + Lead and scale a world-class engineering organization of 100+ data engineers, ML engineers, and platform developers, including managing managers and Staff/Principal-level engineers while driving hiring, retention, and talent development strategies + Provide strategic technical direction for enterprise-scale lakehouse architecture using Databricks Data Intelligence Platform (Unity Catalog, Delta Lake, Delta Live Tables, Mosaic AI) and AWS cloud services, ensuring highly performant, cost-effective, and interoperable data foundations + Drive the organization's transition to production-grade autonomous AI systems, leveraging Databricks AI stack including MLflow, Feature Store, Vector Search, and Model Serving to deliver measurable business value beyond proof-of-concept implementations + Establish rigorous engineering standards and operational excellence practices including SLOs, error budgets, incident management, and infrastructure-as-code using tools such as Terraform and Databricks Asset Bundles for reliable platform delivery + Oversee enterprise semantic modeling and RAG architecture strategies, ensuring AI systems are grounded in Unity Catalog-governed data assets with proper access controls, lineage tracking, and auditability to prevent hallucinations + Foster a high-performance engineering culture rooted in psychological safety, continuous learning, and rapid experimentation while partnering with Product, FinOps, and executive leadership to translate business goals into actionable technical roadmaps, At S&P Global, we are committed to fostering a connected and engaged workplace where all individuals have access to opportunities based on their skills, experience, and contributions. Our hiring practices emphasize fairness, transparency, and merit, ensuring that we attract and retain top talent. By valuing different perspectives and promoting a culture of respect and collaboration, we drive innovation and power global markets. ## Related Videos - [How we built an AI-powered code reviewer in 80 hours](https://www.wearedevelopers.com/videos/1511-how-we-built-an-ai-powered-code-reviewer-in-80-hours) - [Infrastructure as Code: The Developer's Secret Weapon](https://www.wearedevelopers.com/videos/1221-infrastructure-as-code-the-developer-s-secret-weapon) - [WeAreDevelopers LIVE - CSS is DOOMed](https://www.wearedevelopers.com/videos/1838-wearedevelopers-live-css-is-doomed) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Implementing Feature Environments with AWS and Terraform](https://www.wearedevelopers.com/videos/531-implementing-feature-environments-with-aws-and-terraform) - [Tips, Techniques, and Common Pitfalls Debugging Kafka](https://www.wearedevelopers.com/videos/838-tips-techniques-and-common-pitfalls-debugging-kafka) ## Related Articles - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)