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First American

Title insurance and real estate services

Director of Engineering - Automation

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeSanta Ana, CA, USA$197.2k - $263k /yrRemote

About the Job

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 machine-learning-based data processing at production scale.
  • Technical fluency in AI and machine-learning lifecycle management, including 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 continuous integration and continuous delivery, 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.
  • 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.

Responsibilities

  • 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, machine-learning-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 development and maintenance of the canonical data model for title production, a unified ground truth spanning manual and automated order workflows and serving as the foundation for machine-learning 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.
  • Own engineering programs expanding 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 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.
  • Define architecture and engineering standards across document processing, event-driven data pipelines, data modeling, machine-learning lifecycle, and the enterprise AI platform.
  • Build a governed, AI-ready data foundation with metadata and lineage, data contracts, lifecycle controls, strong data quality, and open interfaces.
  • Set the domain's approach to responsible AI engineering, including 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.
  • 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 align data and automation capabilities with production outcomes and operational realities.
  • Build an inclusive, psychologically safe environment where engineers can grow, challenge ideas constructively, and deliver results.
  • Lead through engineering managers and develop them into leaders who can own their programs independently.
  • Own organizational health and talent outcomes, including hiring, performance, career development, and succession; raise the talent bar consistently.
  • Own the domain roadmap and investment tradeoffs that shape it, apply return-on-investment discipline, and redirect when returns lag.
  • 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.
  • 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.

Desired Qualifications

  • Experience with modern lakehouse and cloud data warehouse platforms such as Databricks, Snowflake, or equivalents, including Unity Catalog, MLflow, or comparable data and machine-learning governance tooling.
  • Experience with event-driven architectures and data pipeline design for operational and analytical workloads.
  • Experience with document intelligence, optical character recognition, extraction models, schema design, versioning strategies, and accuracy measurement.
  • Experience with open table formats, data catalogs, lineage, semantic modeling, data contracts, and lifecycle management.
  • Experience designing or operating AI platforms that support data science, machine learning, data products, and AI-enabled experiences.
  • Experience in highly regulated industries such as financial services, insurance, or real estate.

About the Company

First American

Title insurance and real estate services

First American provides title insurance, settlement services, and a range of real estate–related products. It supports buyers, sellers, lenders, and real estate professionals with title protection, data, and closing services, plus post-closing and risk management. The company runs a nationwide property-data network (including DataTree) and offers self-service tools and an online portal (myFirstAm) to track orders and access records. It also offers home warranty and homeowners insurance, trust and banking services, and international services for corporate clients.

Company size
10,001+
Company stage
Not listed
Total funding
$800.2M
Headquarters
Santa Ana, CA
Founded
1889

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