Lead Platform Engineer

D.R. Horton is hiring a Lead Platform Engineer to provide hands-on technical leadership for major domains of its enterprise Data & AI Foundation. The role leads platform engineering standards, implementations, operational practices, and cross-team delivery across data services, DataOps, AI/ML capabilities, observability, automation, and hybrid infrastructure.

Responsibilities

• Lead engineering and operational ownership for major data platform domains, establishing technical standards for security, reliability, automation, workload management, observability, resilience, recovery, capacity, and cost governance
• Lead design and implementation of reusable platform services and automation using Python and/or other modern languages, APIs, infrastructure-as-code, CI/CD, Git-based workflows, testing, and configuration management
• Define and drive SRE/DataOps practices including SLIs, SLOs, service tiers, incident response, root-cause analysis, postmortems, automated remediation, availability, and recovery objectives
• Lead technical design for platform capabilities supporting analytics, machine learning, generative AI, semantic search, RAG, agents, model endpoints, vector services, model gateways, and intelligent applications
• Establish operational patterns for AI/ML services including deployment, inference, secrets, workload isolation, quotas, evaluation, monitoring, versioning, rollback, and lifecycle management
• Lead engineering integration across cloud and on-premises infrastructure, including containers/Kubernetes, storage, networking, GPU-enabled compute, resource scheduling, capacity, and observability
• Lead platform FinOps for assigned domains, including consumption analysis, forecasting inputs, utilization, unit economics, license optimization, vendor commitments, and cloud/on-prem workload placement recommendations
• Lead complex incidents, major changes, upgrades, migrations, proofs of concept, and operational-readiness assessments
• Review technical designs and code, mentor engineers, establish reusable patterns, and raise engineering quality across the team
• Partner with architecture, cybersecurity, infrastructure, data engineering, analytics, AI, sourcing, and business teams to resolve cross-platform decisions and dependencies
• Drive measurable improvements in availability, MTTR, automation coverage, self-service adoption, resource utilization, and unit cost
• Represent Data & AI Foundation engineering in architecture, security, governance, audit, sourcing, infrastructure, and strategic planning forums
• Lead technology evaluations and build/buy/partner analysis for assigned platform capabilities, considering interoperability, security, operational complexity, vendor lock-in, and total cost of ownership
• Provide technical leadership without direct supervisory responsibility, coordinate work across engineers and partners, and perform additional duties as assigned
• Conduct all business in a professional and ethical manner

Skills

• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field, or equivalent education and experience
• 7 or more years of progressive experience engineering and operating enterprise cloud or hybrid data platforms, DevOps platforms, AI/ML platforms, databases, integration platforms, or comparable services
• Expert-level experience in one or more platform domains with advanced breadth across security, performance, workload management, observability, resilience, automation, and cost governance
• Strong software and automation skills using Python and/or another modern language, APIs, infrastructure-as-code, CI/CD, Git, automated testing, and configuration management
• Demonstrated technical leadership establishing engineering standards, SRE/DataOps practices, recovery capabilities, operational controls, and platform roadmaps
• Experience leading complex platform implementations, migrations, incidents, architecture decisions, and cross-functional technical workstreams
• Advanced working knowledge of AI/ML platform patterns including LLM deployment, inference, RAG, vector search, model gateways, evaluation, observability, and lifecycle management
• Ability to mentor engineers, influence technical decisions, and clearly communicate platform risk, architecture, value, cost, and trade-offs to senior stakeholders
• Advanced Snowflake, cloud, architecture, security, FinOps, SRE, Kubernetes, or responsible AI certification preferred
• Broad hands-on experience with Snowflake, Azure, Fivetran, dbt, Power BI/Microsoft Fabric, GitHub, Terraform/OpenTofu, Kubernetes, observability platforms, API management, and AI/ML platform services or comparable technologies preferred
• Experience with hybrid cloud/on-premises infrastructure, Linux, GPU-enabled compute, storage, networking, workload scheduling, hardware/software economics, metadata/catalog, governance, and open data formats such as Apache Iceberg preferred

Benefits

• Medical, Dental and Vision
• 401(K)
• Employee Stock Purchase Plan
• Flex Spending Accounts
• Life & Disability Insurance
• Vacation, Sick, Personal Time and Company Holidays
• Multiple Voluntary and Company provided Benefits

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