Director, Engineering - Title Automation

First American•Santa Ana, CA

1d•Remote

About The Position

Join a team that puts its People First! Since 1889, First American (NYSE: FAF) has held an unwavering belief in its people. They are passionate about what they do, and we are equally passionate about fostering an environment where all feel welcome, supported, and empowered to be innovative and reach their full potential. Our inclusive, people-first culture has earned our company numerous accolades, including being named to the Fortune 100 Best Companies to Work For® list for eleven consecutive years. We have also earned awards as a best place to work for women, diversity and LGBTQ+ employees, and have been included on more than 50 regional best places to work lists. First American will always strive to be a great place to work, for all. For more information, please visit www.careers.firstam.com. Be part of a transformative engineering organization that is shaping how First American builds and delivers the AI-driven systems powering modern title production. We are looking for a Director of Engineering to own parts of the First American's title automation strategy. You will lead a distributed engineering organization through engineering managers and senior individual contributors, set technical strategy in partnership with Product, Data Science, and Title Operations, and be accountable for delivery, platform health, talent, and measurable business outcomes across your domain.

Requirements

Experience owning the strategy, architecture, and operational outcomes of a large-scale production data or AI platform serving many teams, workloads, and users.
Deep technical fluency in distributed data processing, data lakes and lakehouses, cloud data warehouses, event-driven architectures, ingestion, orchestration, and production pipelines.
Experience with document intelligence, unstructured data extraction, or ML-based data processing at production scale.
Technical fluency in AI/ML lifecycle management — model evaluation, ground truth governance, production deployment, versioning, and drift detection.
Strong understanding of cloud networking, identity and access, storage, compute, resilience, and cloud-native services.
Technical fluency in Infrastructure as Code and CI/CD, with experience guiding automated provisioning and delivery including quality and governance controls.
Experience modernizing complex data environments and migrating business-critical workloads without disrupting consumers.
Strong architectural and vendor judgment, including evidence-based build/buy tradeoffs across durability, time to value, interoperability, operability, and cost.
Experience shaping operating models and leading distributed teams through technical and people leaders in a matrixed organization.
A record of setting technical strategy, guiding investment decisions, and delivering measurable business outcomes in partnership with Product and business leaders.
Practical expertise in operational excellence, governance, security, data quality, cost management, and performance at scale.
Excellent executive communication and change leadership, with the ability to build alignment and explain the business consequences of technical choices.
Inclusive leadership and a record of developing engineering managers, building a leadership bench, and creating accountability for sustained outcomes.

Nice To Haves

Experience With Modern lakehouse and cloud data warehouse platforms such as Databricks, Snowflake, or equivalents, including Unity Catalog, MLflow, or comparable data and ML governance tooling.
Event-driven architectures and data pipeline design for operational and analytical workloads.
Document intelligence, OCR, extraction models, schema design, versioning strategies, and accuracy measurement.
Open table formats, data catalogs, lineage, semantic modeling, data contracts, and lifecycle management.
Designing or operating AI platforms that support data science, machine learning, data products, and AI-enabled experiences.
Highly regulated industries such as financial services, insurance, or real estate.

Responsibilities

Own the Data and Document Intelligence Platform: Define and evolve the architecture for a production-grade document intelligence system that extracts, structures, and governs data from title insurance documents at scale — turning unstructured document images into versioned, reusable, ML-ready data assets.
Own the platform that emits title production events to the data lake, decoupling operational systems from analytics and enabling downstream modeling and data science consumption.
Lead the development and maintenance of the canonical data model for title production — a unified ground truth that spans both manual and automated order workflows and serves as the foundation for ML training, evaluation, and automation.
Drive document classification programs, including expanding scope across new document types and geographies.
Partner with data science and AI governance teams to maintain labeled training datasets, accuracy benchmarks, and evaluation standards that underpin model trustworthiness.
Scale AI-Powered Title Automation: Own the engineering programs that expand First American's AI-driven title production platform — Sequoia — across new order types, geographies, and title production scenarios.
Lead automated generation of title search packages and related outputs built on top of the structured data platform, moving proof-of-concept programs into production.
Define sequencing, investment tradeoffs, and measurable outcomes for automation programs that directly grow Sequoia's coverage and accuracy.
Set Technical Direction and AI-Ready Architecture: Define architecture and engineering standards across document processing, event-driven data pipelines, data modeling, ML lifecycle, and the enterprise AI platform (Databricks, Unity Catalog, MLflow, or equivalents).
Build a governed, AI-ready data foundation — metadata and lineage, data contracts, lifecycle controls, strong data quality, and open interfaces — that makes automation built on top of it trustworthy.
Set the domain's approach to responsible AI engineering: model evaluation frameworks, ground truth governance, production deployment standards, drift detection, and human accountability.
Drive technical build/buy/modernize decisions, balancing interoperability, time to value, and sound economics.
Operate as a Product Owner and Cross-Functional Partner: Partner with Product on customer needs, desired outcomes, adoption, and experience; own the engineering approach to service boundaries, self-service capabilities, and technical delivery.
Partner with Product on investment tradeoffs, balancing platform modernization, technical debt, delivery, and responsible experimentation.
Collaborate with Title Operations, Data Science, and peer engineering leaders to ensure the data and automation capabilities you build are aligned to production outcomes and operational realities.
Build High-Performing, Distributed Engineering Teams: Build an inclusive, psychologically safe environment where engineers can grow, challenge ideas constructively, and deliver exceptional results.
Lead through engineering managers; develop them into leaders who can own their programs without you in the room.
Own organizational health and talent outcomes including hiring, performance, career development, and succession; raise the talent bar consistently.
Drive Delivery and Modernization Excellence: Own the domain roadmap and the investment tradeoffs that shape it; apply ROI discipline and redirect when return lags.
Lead modernization of production data and document platforms while protecting downstream consumers and maintaining business continuity.
Own engineering capacity planning and transparent intake, making commitments, dependencies, and delivery risks visible while protecting team focus.
Establish Operational Excellence and Cost Discipline: Own operational standards across observability, service levels, incident response, on-call, resilience, disaster recovery, release controls, and platform support.
With Security, Privacy, and Governance partners, enforce production-readiness guardrails for access control, lineage, auditability, governance, and data quality.
Drive cost and performance discipline through metrics, postmortems, and automation that strengthen reliability, efficiency, and customer trust.