Director, Data (Hybrid)

What you’ll bring to the table: (Responsibilities)

• Define and own the data strategy and multi-year roadmap for Data Engineering and Data Science, aligning investments with company objectives in partnership with the CTO and executive team. 
• Establish and enforce architecture standards that ensure consistent designs that produce repeatable results.  
• Assess the current state of our data infrastructure — evaluate what needs refactoring, and what should be replaced — then execute on that plan with the team. 
• Lead the continued build-out of the modern data stack, including ELT pipelines, stream ingestion, transformation logic along with storage and compute — remaining hands-on in design and implementation while serving as the final technical authority on data platform decisions. 
• Hire and develop data talent, grow the team with experienced engineers and scientists and develop the existing team members through mentorship, code review, and technical leadership. 
• Own data governance, including the data model RFC process — reviewing proposed changes, enforcing boundary discipline, and ensuring no undocumented changes promote to canonical layers. 
• Own data pipeline monitoring and observability, ensuring production pipelines have appropriate alerting, data quality checks, and incident response processes so failures are caught early and resolved quickly. 
• Own the data function's budget, including headcount planning, platform and cloud spend, and vendor selection and management. 
• Serve as the data leader to the executive team and cross-functional stakeholders — reporting on progress, ROI, architectural decisions, and risks, and communicating tradeoffs clearly when business commitments are at risk. 

Do you have the right mix of ingredients: (Requirements)

• Bachelor's degree in Computer Science, Engineering, or related field (or equivalent experience) 
• 10+ years of experience across data engineering, data science, or analytics, with a strong hands-on data engineering foundation 
• 4+ years leading data teams, including direct management of both data engineers and data scientists 
• Proven experience leading data science work through the full ML model lifecycle, including MLOps and production deployment 
• Strategic leader who can define a data vision, build an executive-ready roadmap, and tie data investments to measurable business outcomes 
• Expertise in data engineering — including model design, testing strategy, incremental patterns, and layer boundary governance 
• Experience designing and enforcing data modeling standards in a team environment — not just building models, but establishing the patterns others follow 
• Demonstrated ability to build, lead, and retain high-performing teams across engineering and science — through hiring, coaching, code review, and setting standards that stick 
• Familiarity with CI/CD for data pipelines (GitLab or equivalent) — including automated testing, deployment workflows, and environment promotion strategies 
• Experience with data observability and monitoring — alerting on pipeline failures, data quality degradation, and freshness SLAs (familiarity with tools like Elementary, Monte Carlo, or equivalent) 
• Strong decision-making under ambiguity — this role requires someone who can move forward with incomplete information and course-correct, not someone who needs consensus to proceed 
• Clear, direct communicator with executive presence who can influence senior stakeholders and translate technical tradeoffs into business terms 
• Technical expertise in: Python, Apache Airflow, Apache Kafka, cloud data warehouses (Redshift, GBQ, Databricks)  

Education, Certifications and Experience: Preferred 

• Bachelor's or advanced degree in a technical field 
• Experience at a company that has completed a similar legacy-to-modern warehouse migration 
• Experience building and scaling a data organization, including managing managers or technical leads 
• Experience owning a departmental budget, platform spend, and vendor relationships