VP Global Workforce Optimization Data Architecture Lead Analyst
Listed on 2026-02-05
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IT/Tech
Data Engineer, Data Analyst, Data Security, Data Science Manager
Job Family: Data Architecture
About Citi: Citi, a global bank, serves over 200 million customers worldwide. Citi enables clients to achieve strategic financial objectives through cutting‑edge technology and innovative solutions. Citi’s technology teams are transforming how people bank and how business is conducted worldwide.
Please note that we are unable to offer visa sponsorship for this position. All applicants must be legally authorized to work in the United States without company sponsorship, now or in the future.
Who We Are:Citi’s Global Workforce Optimization (GWFO), an organization within Citi’s Chief Operating Office and Enterprise Operations, designs solutions to provide transparency, visibility, and enterprise solutions to drive workforce optimization, improve customer experience, increase revenue, achieve service objectives, and maximize efficiency.
About the Role:The GWFO Data Architecture Senior Analyst is a seasoned, data‑savvy specialist who partners with internal clients to translate data needs into scalable data pipelines, integrations, governance‑aligned datasets, and repeatable onboarding patterns. The role focuses on improving data maturity, simplifying onboarding, and accelerating speed‑to‑market data outcomes.
Responsibilities:- Partner with internal clients to capture data needs, define onboarding approach, and integrate data into the GWFO product suite.
- Own end‑to‑end data onboarding delivery: intake, requirements, mapping, integration design, build, validation, and release.
- Establish repeatable onboarding data playbooks and templates that simplify GWFO data maturity and reduce time‑to‑value.
- Manage relationships with key stakeholders across the organization, including day‑to‑day execution of activities and meetings.
- Lead larger projects with broader impact; interact and onboard new clients, partner with existing clients, and focus on integrating client data into our product suite for workforce solutions.
- Liaise with multiple data teams/departments, technology partners, and serve as subject matter for planning and analysis of project deliverables and processes for data initiatives.
- Partner with technology and data teams to design and implement scalable data flows and integration patterns across platforms to support workforce solutions.
- Reduce manual data consumption through automation, self‑service enablement, and resilient engineering solutions in conjunction with technology.
- Translate complex data problem statements into actionable designs (source‑to‑target mappings, data models, and integration specifications).
- Improve data effectiveness by implementing pragmatic checks and standards that increase trust, reuse, and scalability.
- Lead data tooling and evaluations to optimize onboarding, metadata discovery, data access, and quality needs (e.g., data catalogs, Snowflake, data quality rules, cloud solutions, semantic layers, medallion architecture).
- Define hypotheses, success criteria, and scale recommendations; communicate outcomes and adoption paths to stakeholders.
- Examine enterprise data frameworks to identify opportunities that simplify onboarding and accelerate GWFO data maturity aligned to Citi strategic roadmaps.
- 6+ years of demonstrated experience in data engineering, architecture, or related field with a history of successfully delivering complex projects.
- 5+ years of experience in data frameworks, data pipeline and data modeling, cloud or relational database concepts, building data roadmaps and strategies, and providing executives with strategic outcomes and updates.
- Prior experience in banking industry or financial services sector is highly desirable.
- BS in Computer Science, Finance, Engineering, Math preferred.
- Experience in analyzing and defining data structures and architecture.
- Experience with databases and coding languages (SQL, SAS, Python, R, etc.).
- Experience with emerging data trends and technologies, including semantic layers, medallion data architecture, and scalable data frameworks.
- Strong understanding of Enterprise Data Architecture frameworks and methodologies.
- Ability to interact with all levels, including…
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