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Mercury Insurance

Auto and home insurance via agents

Director – Data Warehouse Engineering

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeRemote in USA

About the Job

Requirements

  • A bachelor's degree in Data Science, Data Engineering, Computer Science, Mathematics, Statistics, Engineering, Information Systems, Business Administration, or a related technical field is required.
  • A master's degree is preferred, or an equivalent combination of education and experience.
  • At least 15 years of experience in data engineering, data warehousing, or related disciplines, including significant experience leading high-performing engineering teams and managers.
  • At least 10 years of people leadership experience, including coaching leaders, building teams, setting expectations, and creating accountability.
  • Proven success leading enterprise-scale data warehouse or lakehouse platforms in complex business environments.
  • Deep expertise in enterprise data architecture and data modeling.
  • Experience redesigning and migrating foundational data pipelines and warehouse structures involving thousands of tables, pipelines, and feeds.
  • Experience partnering with cross-functional stakeholders and senior leaders to prioritize roadmaps, resolve trade-offs, and deliver business value.
  • Ability to lead production support for EDS and support mandatory data requirements.
  • Mastery of 3NF, dimensional, star, and snowflake modeling approaches, and judgment to apply an appropriate model for business needs.
  • Strong understanding of operational excellence in data engineering, including testing, data quality frameworks, observability, incident reduction, and service reliability.
  • Expert-level SQL proficiency and strong Python proficiency.
  • Production experience with Informatica, DBT, orchestration platforms such as Airflow, Tivoli, or Dagster, and Git-based development workflows.
  • Hands-on experience with cloud data warehouse or lakehouse technologies such as Redshift, Databricks, Snowflake, or BigQuery.
  • Familiarity with layered (medallion) data architecture patterns.
  • Strong experience with CI/CD, automation-first engineering practices, and scalable software delivery in cloud environments such as AWS, GCP, or Azure.
  • Strong communication skills and ability to influence technical and non-technical audiences without authority.
  • Experience using AI tools such as OpenAI, Claude, or Gemini to solve operational or engineering problems.
  • A bachelor's degree in Computer Science or a related field, or equivalent practical experience; a master's degree is preferred.
  • Experience in insurance, SaaS, or marketplace environments is a plus.

Responsibilities

  • Define and lead the vision, roadmap, and operating model for data warehouse engineering in support of the enterprise data strategy.
  • Lead multiple teams or a broader engineering function responsible for enterprise data warehouse development, data marts, core data models, and scalable data pipelines.
  • Modernize the data platform, including data modeling standards, orchestration patterns, testing frameworks, observability, and automation to improve efficiency.
  • Establish engineering standards for reliability, performance, scalability, data quality, and maintainability across the warehouse and pipeline ecosystem.
  • Collaborate with engineering, technology, and data science teams to establish SLAs, data contracts, and data quality standards.
  • Oversee the design and implementation of end-to-end data processing solutions supporting enterprise reporting, business operations, data science, and AI use cases.
  • Partner with senior leaders across Engineering, Data Science, Architecture, and business teams to align investments, priorities, and delivery plans.
  • Translate business goals into data platform capabilities, roadmap delivery, and measurable outcomes.
  • Lead the evolution of enterprise data models, including decisions on grain, entities, relationships, conformed dimensions, and slowly changing dimensions.
  • Drive a data product mindset, helping teams move from reactive ticket-based delivery to scalable, reusable data capabilities.
  • Build an engineering culture grounded in ownership, continuous improvement, automation, and disciplined execution.
  • Mentor and develop managers, senior engineers, and technical leads while strengthening succession planning and organizational capability.
  • Build and manage multiple teams to execute the data strategy.
  • Guide capacity planning, prioritization, vendor and tool decisions, and resource allocation across the function.
  • Partner with engineering teams to productionize pipelines and integrations with strong service levels, resilience, and operational support.
  • Champion governance, controls, and practices that improve trust in enterprise data and reduce manual effort and technical debt.
  • Identify opportunities to use AI and automation to improve developer productivity, reduce repetitive work, and accelerate delivery.

Desired Qualifications

  • At least five years of experience leading large-scale data warehouse overhauls, pipeline migrations, and performance improvement initiatives across legacy platforms and modern cloud architectures.
  • Strong technical and strategic background evaluating Redshift, Snowflake, Databricks, or similar platforms, recommending a path forward, and building stakeholder alignment.
  • Experience leading and developing data engineering organizations, including inheriting and guiding teams responsible for warehouse architecture, ETL performance, and execution at scale.
  • Experience leveraging AI to drive process efficiencies in optimization, modernization, management, and performance.
  • Experience in insurance, SaaS, or marketplace environments.

About the Company

Mercury Insurance

Auto and home insurance via agents

Mercury Insurance provides auto, home, condo, renter, and business insurance through a network of local independent agents. Its products are selected with guidance from agents and include options like mechanical protection; pricing is based on multiple factors rather than a single rate. Mercury differentiates itself with an independent agency model coupled with factor-based pricing and a long-standing asset base, supporting growth across several states. Its goal is to help people reduce risk and overcome unexpected events by offering reliable coverage and timely assistance through its agent network.

Company size
1,001-5,000
Company stage
IPO
Headquarters
Brea, CA
Founded
1962

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