Senior Director, Data & Analytics

We need to output HTML fragment only, with sections for headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Use

for each heading only if there is real specific content. We must not repeat job title as heading. Must not include Job ID etc. Must preserve all substantive info, not invent or summarize away detail. Remove boilerplate, navigation, legal, EEO, scam-warning, duplicated lines, application instructions. Also we must not include sections with filler like "commensurate with experience". We need to extract content. Let's parse the job description. First paragraph: "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." That's about the role. Could be "About the role". We'll put that in

About the role

then

with that content. Next: "What You’ll Do" list: bullet points. Those are responsibilities. So we need a Responsibilities section with

• each bullet. List: - 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, fostering technical excellence, innovation, collaboration, and continuous improvement. That's responsibilities. Next: "What You’ll Bring" list: qualifications/requirements. This includes experience, skills, education. We need to split into Requirements, Qualifications, Skills maybe. The instruction: Use headings for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to decide what content goes where. We have "What You’ll Bring" which includes: - 10-plus years of progressive experience in data engineering, analytics, data science, or related technology disciplines, including enterprise cloud and data solutions. - 5-plus years of senior leadership experience managing multidisciplinary data, analytics, engineering, or technology teams, including people managers. - Strong expertise in enterprise data architecture, lakehouse platforms, data integration, dimensional and semantic modeling, ETL/ELT, and data warehousing. - Deep experience with Databricks, Microsoft Azure, SQL, Python, and PySpark. - Working knowledge of machine learning, MLOps, generative AI, feature stores, model deployment, monitoring, and AI governance. - Demonstrated experience taking analytics, data science, or AI initiatives from prototype through production and measurable business impact. - Strong understanding of data quality, governance, security, metadata, lineage, compliance, and data residency requirements. - Experience managing technology budgets, vendors, platform costs, and cross-functional initiatives. - Experience leading distributed or global teams across multiple regions and time zones. - Excellent executive communication, strategic thinking, problem-solving, and leadership skills. - Bachelor’s degree in computer science, engineering, statistics, or a related quantitative discipline; equivalent experience may be considered. Advanced degree preferred. Then "Preferred Qualifications" list: - Experience with Elite 3E and related professional services or practice management data structures. - Experience with Databricks, Unity Catalog, Microsoft Fabric, Power BI, Denodo, and emerging AI or agentic AI platforms. - Experience in a law firm or professional services environment, including client confidentiality, matter-level data, data residency, and regulatory requirements. We need to map these to sections: Requirements (mandatory), Qualifications (maybe preferred?), Skills (technical skills). But we must not create sections with filler. We have content for each. We could do: - Requirements: the mandatory experience/education items (first list). - Qualifications: the preferred qualifications list. - Skills: maybe the technical skills list (Databricks, Azure, SQL, Python, PySpark, machine learning, MLOps, etc.) but those are already in the first list. Could separate but risk duplication. Better to put technical skills under Skills, and experience/education under Requirements, and preferred qualifications under Qualifications. However the first list also includes skills like "Strong expertise in enterprise data architecture..." which is skill. But we can still put them under Requirements as they are required. The instruction: Prefer a few substantive sections over many thin ones. So maybe we combine Requirements and Qualifications into one section? But we have headings list; we can include both if they have substantive content. We need to avoid repeating info. Let's think: We could have: - Requirements: mandatory experience, education, leadership. - Skills: technical expertise (Databricks, Azure, SQL, Python, PySpark, machine learning, MLOps, generative AI, etc.) - Qualifications: preferred qualifications (Elite 3E, Unity Catalog, etc.) But there is overlap: the first list includes "Strong expertise in enterprise data architecture, lakehouse platforms, data integration, dimensional and semantic modeling, ETL/ELT, and data warehousing." and "Deep experience with Databricks, Microsoft Azure, SQL, Python, and PySpark." and "Working knowledge of machine learning, MLOps, generative AI, feature stores, model deployment, monitoring, and AI governance." Those are skills. So we could put those under Skills. The experience/years and leadership under Requirements. Education under Requirements as well. Thus: Requirements: 10+ years experience, 5+ years senior leadership, Bachelor's degree (or equivalent), advanced degree preferred. Skills: Strong expertise in enterprise data architecture, lakehouse platforms, data integration, dimensional and semantic modeling, ETL/ELT, data warehousing; Deep experience with Databricks, Microsoft Azure, SQL, Python, PySpark; Working knowledge of machine learning, MLOps, generative AI, feature stores, model deployment, monitoring, AI governance; Strong understanding of data quality, governance, security, metadata, lineage, compliance, data residency; Experience managing technology budgets, vendors, platform costs, cross-functional initiatives; Experience leading distributed/global teams; Excellent executive communication, strategic thinking, problem-solving, leadership. Qualifications (Preferred): Experience with Elite 3E and related professional services or practice management data structures; Experience with Databricks, Unity Catalog, Microsoft Fabric, Power BI, Denodo, and emerging AI or agentic AI platforms; Experience in a law firm or professional services environment, including client confidentiality, matter-level data, data residency, and regulatory requirements. Now Benefits: The posting includes: "Why Join Ashurst Perkins Coie? ... This position is eligible for an annual discretionary bonus, 401(k) plan, medical, dental, and vision insurance, accrued paid time off plan starting at 20 days annually, personal medical and parental leave, up to ten paid holidays, and family care benefits. More information regarding benefits and programs may be found here." Thus we have Benefits section with those items. Pay: The posting includes compensation ranges for various locations: Colorado, Illinois, Washington state, Washington D.C. Also statement: "This position may be filled in the following locations. The listed compensation range reflects the typical pay for this role, though it is rare for new hires to receive an offer at the top of the range. Actual compensation may vary depending on experience, skills, market conditions, and internal equity". So we have Pay info. Schedule: Not mentioned. No schedule info (like full-time, remote, etc). So we should NOT create a Schedule section because no specific content. Thus we will have sections: About the role, Responsibilities, Requirements, Skills, Qualifications, Benefits, Pay. We must not include sections with filler. Ensure each heading has substantive content. Now we need to output HTML fragment only, no markdown fences. We'll produce:

  About the role

  ... (the about text)

  Responsibilities

  • ...
  ...

  Requirements

  • 10-plus years of progressive experience in data engineering, analytics, data science, or related technology disciplines, including enterprise cloud and data solutions.
  • 5-plus years of senior leadership experience managing multidisciplinary data, analytics, engineering, or technology teams, including people managers.
  • Bachelor’s degree in computer science, engineering, statistics, or a related quantitative discipline; equivalent experience may be considered. Advanced degree preferred.
  But we might also include other requirements like "Experience managing technology budgets, vendors, platform costs, and cross-functional initiatives." and "Experience leading distributed or global teams across multiple regions and time zones." and "Excellent executive communication, strategic thinking, problem-solving, and leadership skills." Those could be considered skills or requirements. However we already have them under Skills. To avoid duplication, we could keep them under Skills. But the instruction says preserve ALL substantive information; we must not lose any. So we need to ensure each piece appears somewhere. If we put them under Skills, that's fine. Thus Requirements list includes only the experience/years and education. That's okay. Now Skills section: we need to list all skill-related items. Let's compile Skills bullet list: - Strong expertise in enterprise data architecture, lakehouse platforms, data integration, dimensional and semantic modeling, ETL/ELT, and data warehousing. - Deep experience with Databricks, Microsoft Azure, SQL, Python, and PySpark. - Working knowledge of machine learning, MLOps, generative AI, feature stores, model deployment, monitoring, and AI governance. - Strong understanding of data quality, governance, security, metadata, lineage, compliance, and data residency requirements. - Experience managing technology budgets, vendors, platform costs, and cross-functional initiatives. - Experience leading distributed or global teams across multiple regions and time zones. - Excellent executive communication, strategic thinking, problem-solving, and leadership skills. Note: "Experience leading distributed or global teams across multiple regions and time zones" appears also in Requirements? Actually it's in What You’ll Bring list after "Experience managing technology budgets...". We'll put under Skills. Now Qualifications (Preferred) section: - Experience with Elite 3E and related professional services or practice management data structures. - Experience with Databricks, Unity Catalog, Microsoft Fabric, Power BI, Denodo, and emerging AI or agentic AI platforms. - Experience in a law firm or professional services environment, including client confidentiality, matter-level data, data residency, and regulatory requirements. Now Benefits section: we need to list benefits as
  • items. Benefits: - Annual discretionary bonus - 401(k) plan - Medical, dental, and vision insurance - Accrued paid time off plan starting at 20 days annually - Personal medical and parental leave - Up to ten paid holidays - Family care benefits