Director, Data Warehouse Engineering

This is a fully remote position, open to applicants in California.

📋 Description

• Define and guide the vision, roadmap, and operating model for data warehouse engineering.

• Oversee multiple teams responsible for developing the enterprise data warehouse, data marts, core data models, and scalable data pipelines.

• Propel the modernization of the data platform, focusing on modeling standards, orchestration, testing, observability, and automation.

• Set engineering standards for reliability, performance, scalability, data quality, and maintainability.

• Establish Service Level Agreements (SLAs), data contracts, and data quality standards in collaboration with engineering, technology, and data sciences teams.

• Manage comprehensive data processing solutions that support reporting, business operations, data science, and AI use cases.

• Collaborate with senior leaders to synchronize investments, priorities, and delivery plans.

• Convert business objectives into data platform capabilities, roadmaps, and measurable outcomes.

• Guide the evolution of enterprise data models, including grain, entities, relationships, conformed dimensions, and slowly changing dimensions.

• Foster a data product mindset and develop scalable, reusable data capabilities.

• Create an engineering culture centered on ownership, continuous improvement, automation, and disciplined execution.

• Mentor and nurture managers, senior engineers, and technical leads.

• Build and manage multiple teams to implement the data strategy effectively.

• Direct capacity planning, prioritization, vendor and tool decisions, and resource distribution.

• Collaborate with engineering teams to productionize pipelines and integrations, ensuring strong service levels, resilience, and operational support.

• Advocate for governance, controls, and best practices to enhance data trust and minimize manual effort and technical debt.

• Identify opportunities for AI and automation to boost developer productivity and speed up delivery.

• Lead production support for EDS and essential data requirements.

⛳️ Requirements

• Bachelor's degree in Data Science, Data Engineering, Computer Science, Mathematics, Statistics, Engineering, Information Systems, Business Administration, or a related technical field.

• Master’s degree is preferred or an equivalent combination of education and/or experience.

• Over 15 years of experience in data engineering, data warehousing, or related fields.

• More than 10 years of demonstrated experience in people leadership.

• Proven track record of leading enterprise-scale data warehouse or lakehouse platforms in complex business settings.

• Extensive knowledge in enterprise data architecture and data modeling.

• Experience in redesigning and migrating foundational data pipelines and warehouse structures encompassing thousands of tables, data pipelines, and feeds.

• Experience collaborating with cross-functional stakeholders and senior leaders.

• Familiarity with 3NF, dimensional, star, and snowflake modeling patterns.

• Strong understanding of testing, data quality frameworks, observability, incident reduction, and service reliability.

• Expert-level proficiency in SQL and strong skills in Python.

• Production experience with tools such as Informatica, DBT, Airflow, Tivoli, or Dagster, and Git-based development workflows.

• Practical experience with Redshift, Databricks, Snowflake, or BigQuery.

• Awareness of layered (medallion) data architecture patterns.

• Strong experience with CI/CD, automation-first engineering, and cloud platforms like AWS, GCP, or Azure.

• Excellent communication skills to engage with both technical and non-technical audiences.

• Experience utilizing AI tools such as OpenAI, Claude, or Gemini.

• A bachelor's degree in computer science or a related field, or equivalent practical experience.

• Experience in insurance, SaaS, or marketplace environments is a plus.

🏝️ Benefits

• Lead a high-impact function that shapes Mercury’s enterprise data foundation.

• Contribute to the modernization of the data platform and influence the future of analytics, AI, and data-driven decision-making across the organization.

• Collaborate with Engineering, Data Science, and business teams on significant initiatives with substantial enterprise impact.

• Build and develop a robust organization while enhancing architecture, delivery, and operational excellence.