Vice President, Utilities Data Owner Lead

Join a dynamic team where data ownership and product delivery come together to create real customer and business impact. You’ll help shape how trusted, well-governed data powers the Digital Utilities product and accelerates analytics at scale. This is an opportunity to grow your leadership, deepen your data governance expertise, and partner closely with product and technology teams. If you enjoy solving complex problems, setting clear standards, and driving outcomes across multiple stakeholders, this role is built for you. Job summary As a Vice President, Utilities Data Owner Lead within the Data & Analytics team, you will define the strategy, governance, and delivery of trusted data across the Digital Utilities product. You will be accountable for data that is created, provisioned, or consumed within the product to support business objectives, advanced analytics, business operations, and reporting. You will partner with product, design, and technology leaders to ensure data is fit for purpose, well-understood, and delivered with strong quality and safety controls. You will build governed, reusable data products that increase speed to insight, improve customer experiences, and enable responsible use of analytics and AI. You will help teams identify and reduce data risk across the data lifecycle while strengthening trust in the product’s data foundation. In this role, you’ll connect data providers and data consumers to ensure shared understanding of definitions, business context, and how data should be used. You’ll drive clarity through strong metadata, lineage, access rules, and measurement definitions so teams can discover and use data confidently. You will influence prioritization across workstreams and timelines, balancing near-term delivery needs with long-term scalability and reuse. Job responsibilities Define and execute the data strategy and roadmap for the Digital Utilities product, delivering governed, reusable data products that support business objectives, operations, advanced analytics, and reporting. Partner with product, design, and technology leaders to ensure data is fit for purpose and supports analytics, machine learning, and generative AI use cases through clear metadata, lineage, access rules, quality controls, and business context. Identify and prioritize critical data domains and elements within the product, ensuring they are documented, classified, and discoverable. Establish expectations for data accuracy, completeness, and timeliness, and coordinate delivery partners to meet data quality requirements and resolve issues. Direct processes to identify, monitor, and mitigate data risks across the lifecycle, including data protection, retention and destruction, storage, use, and quality. Drive execution against milestones and key performance indicators, build strong relationships across business, technology, analytics, operations, risk, and controls, and lead or coach direct and matrixed team members. Required qualifications, capabilities, and skills Six years of experience in a data-related field delivering data products, data programs, or data technology solutions across multiple workstreams. Knowledge of data management, data engineering, pipelines, modeling, architecture, and governance on cloud platforms (for example: AWS, Google Cloud, or Azure) and/or big data technologies (for example: Hadoop, Spark, Alteryx, or Snowflake). Experience working with metadata, taxonomy, measurement definitions, data quality practices, access controls, lineage, and fit-for-use standards. Experience partnering across multiple functions (for example: product, technology, analytics, operations, risk, and controls) to deliver outcomes on shared timelines. Strong written and verbal communication skills with the ability to explain complex technical concepts to senior audiences. Bachelor’s degree. Preferred qualifications, capabilities, and skills Experience building or governing reusable data products (for example: reference data, identity mapping, entitlements, interaction datasets, metrics repositories, or shared enablement datasets). Working knowledge of SQL and at least one analytics, cloud, or visualization tool (for example: Tableau, Python, Snowflake, or Databricks). Knowledge of machine learning and generative AI tools and capabilities. Experience leading through ambiguity, standing up new capabilities, and improving operating models. Master’s degree or relevant certification in data, analytics, cloud, privacy, risk, or data governance.