Manager, Data Engineering - Data & AI Governance

We need to output HTML fragment with sections: About the role (already present), Responsibilities (What You Will Do), Requirements (Education Qualifications, Experience Qualifications, Organizational Relationship maybe?), Benefits, Pay, Schedule. Also maybe "Qualifications" heading? The instruction: use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). We need to include sections only when posting has real specific content. We have content for About the role (the paragraph after "About The Role"). Responsibilities: "What You Will Do" bullet list. Requirements: includes Education Qualifications and Experience Qualifications (maybe combine). Also Organizational Relationship maybe part of Requirements? Could be separate but we can include under Requirements. Benefits: we have benefits paragraph. Pay: we have salary range. Schedule: we have hybrid model description. We must not repeat job title as heading. No Job ID etc. Remove boilerplate: navigation, legal, EEO, scam warning, application instructions, duplicated lines. Also remove "Additional Information" about Fair Chance Ordinance etc? That's legal boilerplate; should be removed. Also "If you are interested..." etc. Also "Pursuant to the Los Angeles County..." etc. Remove. We need to preserve all substantive information. So keep the description about role, responsibilities list, education qualifications, experience qualifications, organizational relationship, benefits, compensation, schedule. We need to format lists with

• . For prose use

  . For headings use

  . We need to decide sections: About the role, Responsibilities, Requirements (maybe combine Education and Experience), Benefits, Pay, Schedule. Could also have Qualifications but we already have Requirements. The instruction says we can use those headings; we should include only those with content. Let's extract content. About the role: The paragraph(s) after "About The Role" up to before "What You Will Do". Let's capture: "The Manager of Engineering – Data & AI Governance is a forward-looking, highly technical, and relentlessly execution-oriented individual who will build a best-in-class governance and data quality capability over the next several years. This is not a traditional governance oversight role, and isn’t centered on administering committees, producing policy documents, or manually tracking compliance. This leader will establish the strategy and personally drive the engineering, automation, operating practices, and organizational adoption required to make trusted, governed, and AI-ready data an embedded characteristic of our enterprise data ecosystem. The mandate is not simply to operate governance more efficiently, but to fundamentally transform governance from a predominantly manual and reactive activity into an intelligent, continuous, measurable, and increasingly automated capability, using modern technology, reusable engineering patterns, and artificial intelligence. Data governance and data quality will be the immediate priorities. The role will also build and mature AI governance in parallel with enterprise AI adoption, creating pragmatic guardrails that manage risk without unnecessarily slowing experimentation, innovation, or business value. The ideal candidate brings meaningful experience in software engineering, data engineering, analytics engineering, and/or other closely related technical disciplines. We are searching for a builder who sees governance as a product and an engineering challenge, not an administrative function, and someone who is energized by an ambitious target, impatient with unnecessary manual work, and willing to personally solve difficult problems. They do not need to be an expert in data management or governance technology, but they must have technical capacity, curiosity, passion, and drive to work hands-on in leading data and AI governance and quality." That's the about role. Responsibilities: The bullet list under "What You Will Do". Each line starts with a verb. We'll capture each as

  . Need to preserve all. List items: - Establish and execute the roadmap to build a best-in-class Data & AI Governance Engineering capability at Crown Castle. - Deliver measurable improvements in data quality, trust, ownership, lineage, accessibility, security, and AI readiness by applying modern practices and eliminating manual, bureaucratic processes. - Balance the long-term governance vision with rapid execution against the highest-priority business needs, by introducing modern practices that improve speed, scale, quality, and business value. - Embed governance by design into data products, pipelines, semantic models, and platform services in partnership with technical, security, audit, and business teams. - Implement and automate metadata, lineage, classification, access, quality, observability, and governance capabilities across Databricks and the broader data ecosystem. - Create reusable standards, controls, and reference patterns while remaining hands-on in prototyping, configuring, testing, troubleshooting, and improving critical capabilities. - Build an automation-first governance strategy that enables a small team to deliver enterprise-scale impact through modern technology and AI. - Develop intelligent workflows and assistants that guide data owners and stewards, automate routine decisions, and reduce the effort required to fulfill governance responsibilities. - Create measurable, exception-based operations that automatically surface risks, prioritize issues, route accountability, and focus the team on the areas requiring human judgment. - Establish clear, measurable expectations for trusted data products, including ownership, business meaning, quality, classification, service levels, and approved use. - Enable users to confidently discover, understand, evaluate, and access governed data through an intuitive enterprise catalog and transparent quality information. - Drive cross-functional resolution of systemic data quality issues by addressing root causes and strengthening accountability at the source. - Establish an enterprise inventory and lifecycle governance model for machine learning, generative AI, agents, and intelligent automation, from initial use-case evaluation through retirement. - Apply proportionate, risk-based governance that accelerates lower-risk innovation while strengthening controls for sensitive data, automated actions, consequential decisions, and material enterprise risks. - Partner across Legal, Security, Privacy, Risk, Audit, Architecture, and the business to establish clear review, approval, monitoring, and accountability for enterprise AI solutions. That's all. Requirements: Education Qualifications and Experience Qualifications. Education Qualifications paragraph: "Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, Statistics, Physics, Computational Science, or another highly quantitative discipline. Master’s or a PhD in a relevant quantitative or computational discipline preferred." Experience Qualifications paragraph: "7 or more years’ experience with a Masters, or 5 or more years’ experience with a Master’s or PhD, of progressively advanced experience across software engineering, data engineering, analytics engineering, data platforms, data governance, data quality, and/or related disciplines. A passion and enthusiasm for data, AI, data governance, data management, and data quality. Demonstrated ability to lead strategy while remaining personally engaged in technical execution. Strong understanding of modern data architecture, data pipelines, data models, metadata, lineage, access controls, data quality, testing, and observability. Working proficiency in one or more technical languages, preferably Python and SQL. Experience automating technical or operational processes through code, APIs, workflow orchestration, CI/CD, and/or platform capabilities. Ability to work hands-on in a modern cloud data environment and quickly learn new technologies. Strong judgment, analytical problem-solving, attention to detail, and personal accountability. Ability to influence across engineering, analytics, architecture, security, legal, risk, audit, and business organizations. Experience preferred with Databricks, Unity Catalog, and associated technologies. Experience preferred implementing data quality, data observability, metadata management, cataloging, lineage, classification, or policy automation. Experience preferred applying software engineering practices to data or governance capabilities, including source control, automated testing, deployment pipelines, infrastructure as code, or reusable frameworks. Experience or familiarity preferred with data products, data contracts, semantic layers, federated governance, and self-service data access. Experience preferred with responsible AI, AI risk management, model governance, generative AI governance, and/or agent governance." Organizational Relationship: "Reports to: Director of Engineering – AI, Data, Analytics, and Governance Title(s) of direct reports (if applicable): One (1) Data Governance Analyst". This could be part of Requirements or separate. We'll include under Requirements maybe as a subpoint. But we can keep as part of Requirements. We could also have a separate section "Organizational Relationship" but not in the list of headings given; but we could still use

  for it? The instruction says use

  for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It doesn't forbid other headings but suggests those examples. Safer to stick to those. So we can fold Organizational Relationship into Requirements. Thus Requirements section will contain education, experience, and organizational relationship. We'll format as

  • for each bullet? The education and experience are paragraphs with multiple sentences; we could keep as

    but the instruction says use

    • for any list of duties/requirements/benefits. So we should convert each distinct requirement into list items. However the education qualifications are two sentences; we could make two list items. Experience qualifications many bullet-like sentences; we can split each sentence into list items. But we must preserve ALL substantive information; not invent or summarize away detail. Splitting into list items is fine as long as we keep the exact text. We'll need to be careful to keep punctuation. Let's extract each sentence. Education: - Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, Statistics, Physics, Computational Science, or another highly quantitative discipline. - Master’s or a PhD in a relevant quantitative or computational discipline preferred. Experience: We have many sentences. Let's list them: 1. 7 or more years’ experience with a Masters, or 5 or more years’ experience with a Master’s or PhD, of progressively advanced experience across software engineering, data engineering, analytics engineering, data platforms, data governance, data quality, and/or related disciplines. 2. A passion and enthusiasm for data, AI, data governance, data management, and data quality. 3. Demonstrated ability to lead strategy while remaining personally engaged in technical execution. 4. Strong understanding of modern data architecture, data pipelines, data models, metadata, lineage, access controls, data quality, testing, and observability. 5. Working proficiency in one or more technical languages, preferably Python and SQL. 6. Experience automating technical or operational processes through code, APIs, workflow orchestration, CI/CD, and/or platform capabilities. 7. Ability to work hands-on in a modern cloud data environment and quickly learn new technologies. 8. Strong judgment, analytical problem-solving, attention to detail, and personal accountability. 9. Ability to influence across engineering, analytics, architecture, security, legal, risk, audit, and business organizations. 10. Experience preferred with Databricks, Unity Catalog, and associated technologies. 11. Experience preferred implementing data quality, data observability, metadata management, cataloging, lineage, classification, or policy automation. 12. Experience preferred applying software engineering practices to data or governance capabilities, including source control, automated testing, deployment pipelines, infrastructure as code, or reusable frameworks. 13. Experience or familiarity preferred with data products, data contracts, semantic layers, federated governance, and self-service data access. 14. Experience preferred with responsible AI, AI risk management, model governance, generative AI governance, and/or agent governance. Organizational Relationship: - Reports to: Director of Engineering – AI, Data, Analytics, and Governance - Title(s) of direct reports (if applicable): One (1) Data Governance Analyst We'll include those as list items under Requirements. Benefits: The paragraph under "Your Company Benefits". We'll keep as

      maybe but instruction says use

      • for any list of benefits. The benefits paragraph is a prose description with multiple sentences; we could keep as

        . But we could also split into list items for each benefit highlighted. However the instruction says use

        • for any list of duties/requirements/benefits. The benefits section is not a list but a description; we can keep as

          . It's okay. Pay: The compensation paragraph: "The salary range offered for this position is $128,200 - $168,210 annually. A candidate's offer is determined by various factors including but not limited to, depth of experience, role-related knowledge and skills, relevant education or training, internal alignment, and work location. Depending on the position offered, the compensation package may also