Director, Data Warehouse Engineering

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

📋 Description

• Define and spearhead the vision, roadmap, and operational framework for data warehouse engineering.

• Manage several teams responsible for the development of the enterprise data warehouse, data marts, core data models, and scalable data pipelines.

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

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

• Collaborate with engineering, technology, and data science teams to set SLAs, data contracts, and data quality standards.

• Oversee comprehensive data processing solutions that support reporting, operations, data science, and AI applications.

• Partner with senior leaders to align investments, priorities, and delivery strategies.

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

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

• Foster a data product mentality and scalable, reusable data capabilities.

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

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

• Construct and oversee multiple teams implementing the data strategy.

• Guide capacity planning, prioritization, vendor and tool selections, and resource distribution.

• Collaborate with engineering teams to operationalize pipelines and integrations with strong service levels and resilience.

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

• Identify AI and automation opportunities to boost productivity and expedite delivery.

⛳️ 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.

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

• More than 10 years of leadership experience, including coaching leaders, building teams, setting expectations, and fostering accountability.

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

• Extensive expertise in enterprise data architecture and data modeling.

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

• Experience collaborating with cross-functional stakeholders and senior leaders to prioritize roadmaps, resolve trade-offs, and deliver business value.

• Experience in providing production support for EDS and mandatory data requirements.

• Mastery of 3NF, dimensional, star, and snowflake modeling patterns.

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

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

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

• Hands-on experience with Redshift, Databricks, Snowflake, or BigQuery.

• Familiarity with layered (medallion) data architecture patterns.

• Significant experience with CI/CD, automation-first engineering, and scalable cloud software delivery in AWS, GCP, or Azure.

• Strong communication skills with the ability to influence both technical and non-technical audiences without authority.

• Experience using OpenAI, Claude, or Gemini for operational or engineering challenges.

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

• Experience in insurance, SaaS, or marketplace environments is advantageous.

🏝️ Benefits

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

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

• Build and expand a strong organization while elevating standards on architecture, delivery, and operational excellence.