Senior Director, Data & Analytics

About the role

Senior Director of Data & Analytics Driving Data Operations. Delivering Business Value. At Ashurst Perkins Coie US LLP, data and analytics are critical to delivering exceptional client service and advancing the firm’s strategic goals. The Senior Director of Data & Analytics leads the firm’s global data and analytics organization and owns the enterprise data strategy, establishing data as a trusted, governed, and reusable firm asset. This role sets the multi-year vision, architecture, and investment roadmap for the enterprise data estate and leads data architecture, engineering, governance, analytics, data science, and machine learning engineering as a connected function. The Senior Director is accountable for the security, reliability, scalability, performance, and cost efficiency of the firm’s data platforms and for delivering trusted data products, insights, and AI capabilities.

Join a collaborative, forward-thinking firm where data, analytics, and technology play an essential role in supporting attorneys, clients, and business operations. As Senior Director, Data & Analytics, you’ll have the opportunity to shape the firm’s enterprise data strategy, modernize data and analytics capabilities, enable responsible AI adoption, and lead a global team delivering trusted data and measurable business value across the firm.

Responsibilities

• Lead the firm’s enterprise data and analytics strategy, roadmap, operating model, and investment priorities in partnership with the Chief Digital & Data Officer and firm leadership.
• Oversee data architecture, engineering, governance, analytics, data science, machine learning engineering, and enterprise data platforms.
• Own the architecture, security, reliability, scalability, performance, and cost efficiency of the firm’s Databricks and Microsoft Azure lakehouse environment.
• Establish enterprise standards for data integration, modeling, quality, governance, metadata, lineage, access, security, and compliance.
• Direct machine learning engineering and MLOps practices, ensuring models and data products can move reliably from development to production.
• Enable analytics, AI, and self-service capabilities through trusted data products, semantic models, and governed data services.
• Lead build, buy, and partner decisions, including vendor management, technology investments, platform optimization, and technical debt reduction.
• Partner with business and technology leaders to identify opportunities for data, analytics, and AI and translate technical priorities into measurable business value.
• Manage budgets, resources, platform consumption, risks, and executive reporting across the data organization.
• Lead and develop a high-performing global organization across multiple regions and time zones, fost