Vice President – Strategy Lead for Data Development Lifecycle (DDLC)

The Chief Data Office helps teams across the firm maximize the value of data through strong governance, clear standards, and scalable delivery practices. You will join a team focused on improving how data and AI solutions move from idea to production with the right controls and a strong practitioner experience. This role offers broad exposure across product, engineering, architecture, and risk partners, with opportunities to shape how teams build and operate data products at scale.

As a Vice President-Strategy Lead for Data Development Lifecycle in the Chief Data Office, you will define and drive adoption of the target-state Data Development Lifecycle across the firm. You will establish standards, reference patterns, and user-centric ways of working that enable safe and efficient data and AI development at scale. You will partner with business, corporate function, and technology stakeholders to remove execution friction and deliver measurable lifecycle improvements through influence. Success requires fluency in modern data science and data engineering practices, along with strong communication and operating-model leadership.You will help connect experimentation-to-production workflows with consistent environments, automated testing, release governance, and production support models. You will balance speed and control by embedding controls-by-design and automation-first approaches into how teams develop, validate, release, and operate data products.

• Define and continuously improve the target-state data development lifecycle across intake, design, build, test, release, and run, including decision rights and exception management
• Partner with architecture and engineering leaders to establish reference architectures and reusable patterns that enable secure, controlled development and testing environments
• Drive roadmap, adoption, and outcomes for lifecycle-enabling platforms and toolchains by translating practitioner needs into clear product requirements, prioritization, and rollout plans
• Enable adoption through playbooks, training, communications, change champions, and practitioner feedback loops
• Embed and automate controls-by-design into the lifecycle in partnership with risk and control stakeholders, using automation-first approaches where possible
• Facilitate cross-functional working groups to align stakeholders and remove execution friction across product, engineering, architecture, risk, and controls
• Identify, escalate, and help mitigate risks and dependencies that could impair strategy execution; manage trade-offs and sequencing
• Produce executive-ready materials to communicate complex concepts and drive decisions with senior leadership
• Operate independently in a highly collaborative, matrixed environment to build consensus and deliver measurable change

• At least eight years of experience in financial services, management consulting, and/or large-scale data or technology transformation
• Strong technical fluency across data analytics, data science, data engineering, and modern data platforms, with the ability to translate between practitioners and senior stakeholders
• Practical knowledge of data product concepts and modern distributed data patterns, including integration between software and data development lifecycles
• Demonstrated experience driving cross-functional operating model or process change and scaling adoption across multiple organizations
• Demonstrated ability to influence stakeholders and align diverse groups without direct authority
• Strong written and verbal communication skills, including executive-level communication of complex technical topics
• Experience improving lifecycle capabilities for data products, including continuous integration and delivery enablement, environment promotion strategies, automated testing approaches, release governance, and production support operating models
• Experience partnering with engineering and platform teams to deliver measurable workflow improvements (for example: reduced onboarding time, faster release cadence, improved reliability, and increased control automation coverage)

• Experience supporting data science and AI development workflows (for example: reproducibility, model validation, monitoring, and experimentation-to-production practices)
• Experience designing or scaling control frameworks that improve compliance outcomes while reducing practitioner friction
• Experience working in a large, highly regulated environment with complex technology landscapes
• Experience developing reusable reference patterns and standards that improve developer experience and consistency