Note: The job is a remote job and is open to candidates in USA. Mercury Insurance is seeking a Director of Data Warehouse Engineering to lead the strategy, architecture, and delivery of its enterprise data warehouse platform. The role oversees teams developing scalable data platforms, enterprise data models, and reliable pipelines while partnering with engineering, data science, product, and business stakeholders to support analytics, reporting, operations, and AI-enabled solutions.
Responsibilities
- Define and lead the vision, roadmap, and operating model for data warehouse engineering in support of Mercury’s 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
- Drive modernization of the data platform, including data modeling standards, orchestration patterns, testing frameworks, observability, and automation to optimize efficiency
- Establish engineering standards for reliability, performance, scalability, data quality, and maintainability across the warehouse and pipeline ecosystem
- Cross-collaborate with engineering/technology and data sciences teams to establish SLAs, data contracts, and data quality standards
- Oversee the design and implementation of end-to-end data processing solutions that support 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 a clear portfolio of data platform capabilities, delivery of roadmaps, 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 across the organization, helping teams move from reactive ticket-based delivery to scalable, reusable data capabilities
- Build a strong 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 on establishing data strategy
- Guide to 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 best 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
Skills
- · Bachelor's degree in Data Science, Data Engineering, Computer Science, Mathematics, Statistics, Engineering, Information Systems, Business Administration, or related technical field
- 15+ years of experience in data engineering, data warehousing, or related disciplines, with significant experience leading high-performing engineering teams and managers
- 10+ years of strong 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
- Redesign and migration of foundational data pipelines and warehouse structures involving 1000s of tables, data pipelines and feeds
- Experience partnering with cross-functional stakeholders and senior leaders to prioritize roadmaps, resolve trade-offs, and deliver business value
- Lead production support for EDS and support mandatory data requirements
- Mastery of modern modeling approaches, including 3NF, dimensional, star, and snowflake patterns, with the judgment to apply the right model for the business need
- Strong understanding of operational excellence in data engineering, including testing, data quality frameworks, observability, incident reduction, and service reliability
- Expert-level proficiency in SQL and strong proficiency in Python
- Production experience with modern data engineering tools and practices, including 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 with the ability to influence technical and non-technical audiences without authority
- Experience using AI tools such as OpenAI, Claude, or Gemini to solve high-value operational or engineering problems
- Bachelor's degree in computer science or a related field, or equivalent practical experience
- Master's degree preferred or equivalent combination of education and/or experience
- Master's degree preferred
- Experience in insurance, SaaS, or marketplace environments is a plus
Benefits
- Lead a high-impact function that shapes Mercury’s enterprise data foundation.
- Help modernize the data platform and influence the future of analytics, AI, and data-driven decision-making across the company.
- Work across Engineering, Data Science, and business teams on visible initiatives with meaningful enterprise reach.
- Build and grow a strong organization while raising the bar on architecture, delivery, and operational excellence.
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