Director, Engineering – Title Automation

First American

. Own the Data and Document Intelligence Platform architecture and operations .

Posted 9/16/2026full-timeRemote • California • United StatesLead💰 $197,200 - $263,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in owning and evolving large-scale production data and AI platforms, with a focus on document intelligence, data governance, and operational excellence. Proven ability to lead distributed engineering teams, drive architectural standards, and ensure production readiness while managing complex data environments.

Highest-signal resume keywords

Data Architecture OwnershipDocument Intelligence ExpertiseAI/ML Lifecycle ManagementCloud Data WarehousingOperational Excellence

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Distributed Data ProcessingData LakesEvent-Driven ArchitecturesML-Based Data ProcessingInfrastructure as CodeCI/CDData Quality ManagementSchema DesignData ContractsPerformance Metrics

Soft Skills

Executive CommunicationChange LeadershipInclusive Leadership

Tools & Technologies

DatabricksSnowflakeUnity CatalogMLflowOCRData Catalogs

Industry Keywords

Document IntelligenceUnstructured Data ExtractionGovernanceCloud-Native ServicesRegulated Industries

Tech Stack

Tools & technologies

CloudUnity

About the role

Key responsibilities & impact

Own the Data and Document Intelligence Platform architecture and operations
Define and evolve a production-grade document intelligence system that extracts, structures, and governs title insurance document data at scale
Own the platform emitting title production events to the data lake
Lead development and maintenance of the canonical data model for title production
Drive document classification programs across new document types and geographies
Partner with data science and AI governance teams on labeled datasets, accuracy benchmarks, and evaluation standards
Own engineering programs expanding the Sequoia AI-driven title production platform across order types, geographies, and scenarios
Lead automated generation of title search packages and related outputs, moving proof-of-concept programs into production
Define sequencing, investment tradeoffs, and measurable outcomes for automation programs
Set architecture and engineering standards across document processing, event-driven data pipelines, data modeling, ML lifecycle, and enterprise AI platforms
Build a governed, AI-ready data foundation with metadata, lineage, data contracts, lifecycle controls, data quality, and open interfaces
Establish responsible AI engineering practices, including evaluation frameworks, ground truth governance, deployment standards, drift detection, and human accountability
Drive technical build/buy/modernize decisions
Partner with Product on customer needs, outcomes, adoption, service boundaries, self-service capabilities, and delivery
Collaborate with Title Operations, Data Science, and engineering leaders
Build inclusive, psychologically safe, distributed engineering teams
Lead through engineering managers and develop them into leaders
Own hiring, performance, career development, succession, and organizational health
Own the domain roadmap, investment tradeoffs, capacity planning, intake, commitments, dependencies, and delivery risks
Lead modernization while protecting downstream consumers and business continuity
Own operational standards for observability, service levels, incident response, on-call, resilience, disaster recovery, release controls, and platform support
Enforce production-readiness guardrails for access control, lineage, auditability, governance, and data quality
Drive cost and performance discipline through metrics, postmortems, and automation

Requirements

What you’ll need

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, 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 CI/CD
Experience modernizing complex data environments and migrating business-critical workloads without disrupting consumers
Strong architectural and vendor judgment, including evidence-based build/buy tradeoffs
Experience shaping operating models and leading distributed teams through technical and people leaders in a matrixed organization
Record of setting technical strategy, guiding investment decisions, and delivering measurable business outcomes 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
Inclusive leadership and a record of developing engineering managers and building a leadership bench
Ideally, experience with Databricks, Snowflake, Unity Catalog, MLflow, event-driven architectures, document intelligence, OCR, extraction models, schema design, open table formats, data catalogs, lineage, semantic modeling, data contracts, AI platforms, and regulated industries

Benefits

Comp & perks

Medical insurance
Dental insurance
Vision insurance
401k
PTO/paid sick leave
Employee stock purchase plan
Inclusive workplace and equal opportunity employment