Skip to content
Level

State Street

Vice President, Finance Data Product Management – P&L

State Street is hiring a Vice President, Finance Data Product Management – P&L in Bengaluru, India. Level rates it ; you can apply on State Street.

Level's read on this job

  • Level rates this finance role Little AI ●○○○: aI is not part of the work.
  • Tools and skills the posting cites include Databricks.
  • Level classifies it as an executive role.
  • Level lists 1,100+ other open roles at State Street.
  • The role was posted in the last few days.

AI in this role

databricks

The VP, Finance Data Product Owner – P&L Data (India) is the execution lead for the P&L data product agenda on One Data Foundation, supporting the VP, Global Data Product Owner – P&L. The role starts with Revenue and Expense Accrual data and sits where Finance, Accounting and Technology meet. It works day to day with data producers, consumers and P&L process owners’ teams, and turns the Global Product Owner’s priorities into certified, reusable data products.

The leader works with process owners to understand how the data is used and what outcome it supports and documents the manual processes and adjustments on both the production and consumption sides. The goal is to raise productivity and efficiency and keep the data products golden by reducing manual manipulation and point-to-point sourcing. The role builds governance by design (CDEs, definitions, ownership, lineage, DQ rules and controls) into each product and uses AI and automation to modernize a largely waterfall and manual delivery lifecycle.

The Global Data Product Owner sets product strategy, priorities, the portfolio roadmap and senior stakeholder agreements. This role executes, recommends and escalates. Controllers, Global Accounting and FP&A own the process and the reported numbers, and Technology owns engineering and production operations.


KEY ACCOUNTABILITIES

1. Bridge Producers and Consumers

  • Be the working-level link between upstream accrual data producers and downstream consumers, so issues are raised once, routed to the right producer and tracked to closure.
  • Partner with P&L process owners’ teams (Controllers, Global Accounting, FP&A and other consumers) to understand how they use the data and the outcome they need. Surface pain points and new needs early to the Global Product Owner.
  • Draft and maintain producer–consumer terms: definitions, scope, frequency, granularity, quality thresholds, ownership and issue handling, for approval by the Global Product Owner.
  • Support the consumer forum by preparing the agenda and facts, and make differences in how consumers interpret or override the same data visible.

2. Understand and Reduce Manual Work

  • Document the manual processes, adjustments and overrides on the production and consumption sides of each in-scope P&L process. Quantify the effort and find the root causes.
  • Recommend and drive fixes: move adjustments upstream or into the product so they are made once and governed, remove spreadsheet dependencies, and stop point-to-point sourcing. Priorities are agreed with the Global Product Owner.
  • Use AI and automation to speed requirements, metadata, CDE identification, lineage, DQ rules, documentation and testing, within agreed controls.

3. Execute the P&L Data Products

  • Run day-to-day delivery of the accrual data products. Maintain the backlog, define requirements, and work with architecture and engineering on sources, transformations, business rules and lineage.
  • Prepare each product for certification: definitions, CDEs, lineage, DQ rules and controls complete, reconciled to the authoritative source, and confirmed to meet accounting requirements.
  • Fix issues at the source with producers, strengthen reconciliation, and maintain a shared business glossary.
  • Publish and reuse on One Data Foundation, enhancing existing products before creating new ones, and support retiring legacy sources and BI reports in favor of certified data.

4. Report, Escalate and Lead

  • Give the Global Product Owner clear, regular updates on progress, risks and decisions needed. Escalate early and prepare materials for senior stakeholder discussions.
  • Work across time zones with agreed overlap hours, documented handoffs and clear written follow-up.
  • Guide and develop team members in India as the work grows.

REQUIRED QUALIFICATIONS

  • Significant banking or financial services experience in Finance, Accounting or P&L functions.
  • Strong working knowledge of revenue and/or expense accruals, accounting concepts, controls and reconciliations.
  • Solid grasp of data lineage, quality and governance principles, with the ability to analyze and validate data independently (for example, SQL) and reconcile it to source.
  • Strong analytical and problem-solving skills, including the ability to analyze manual processes and adjustments to find root causes and efficiency opportunities.
  • Experience working between business and technology teams, with the ability to influence without direct authority and work effectively with global stakeholders across time zones.
  • Clear written and verbal communication that explains data issues in terms of business impact.
  • Demonstrated curiosity and a record of improving ways of working through automation or AI.

Preferred

  • Hands-on experience with Databricks, Power BI or similar platforms; working knowledge of SQL and data modeling.
  • Exposure to billing, revenue or general ledger systems and how they integrate with reporting platforms.
  • Experience with platform migration or re-engineering programs, and with leading complex, multi-team work.
  • Familiarity with GenAI or machine learning use cases in Finance, and agile or product ownership experience.
  • Background in regulated or audit-sensitive environments, including regulatory and external reporting.
  • Professional qualification (CA, CPA, CFA) or a degree in Finance, Accounting, Data or a related field.

MEASURES OF SUCCESS

  • Delivery: accrual data products prepared and certified against the agreed plan, with documented producer–consumer terms.
  • Manual work: manual processes and adjustments documented for each in-scope P&L process, with a measurable reduction in manual manipulation, spreadsheet dependencies and point-to-point feeds.
  • Consistency and reuse: one agreed definition per P&L measure, more consumers on the certified products, and fewer report-level overrides.
  • Issue efficiency: fewer duplicate issues sent to producers and faster resolution.
  • Reliability: timely updates, early escalation and clean handoffs, as rated by the Global Product Owner.
  • Partnership: process owners and producers report the role is fair, responsive and effective.

LEADERSHIP PROFILE AND CANDIDATE CALIBRATION


1. Bridge-builder mindset (most important)

What good looks like

Sits between producers and consumers by choice. Explains each side’s constraints to the other, gets both to commit to a shared agreement, and follows up until it holds.

Watch-outs in candidates

Acts as a requirements taker or a ticket router. Describes the role as “gathering requirements” or “sending tickets.”

2. Curious about the manual work

What good looks like

Wants to see how consumers really use the data and finds the adjustments and spreadsheets behind it. Asks why until the root cause is clear.

Watch-outs in candidates

Accepts manual adjustments as normal. Reports what people do without asking why.

3. Ownership of execution

What good looks like

Acts as an owner, not an order-taker. Follows through, flags problems early and keeps the Global Product Owner informed without being asked.

Watch-outs in candidates

Waits for detailed direction or makes portfolio-level commitments without agreement from the Global Product Owner.

4. Reuse-first and governance by design

What good looks like

Defaults to one certified source and one definition. Builds CDEs, lineage, DQ rules and controls in as the product is built. Challenges point-to-point feeds and local copies.

Watch-outs in candidates

Builds a new dataset for each request. Treats governance as a separate sign-off step.

5. Curious and AI-forward

What good looks like

Questions waterfall and manual ways of working. Experiments with AI and automation and keeps controls intact.

Watch-outs in candidates

Defends the current process. Views AI as hype or as a replacement for judgment.

6. Works well across time zones

What good looks like

Communicates proactively and in writing. Builds trust with global partners, uses facts and resolves conflict early.

Watch-outs in candidates

Communicates only when asked, or escalates as the first move.


About State Street

Across the globe, institutional investors rely on us to help them manage risk, respond to challenges, and drive performance and profitability. We keep our clients at the heart of everything we do, and smart, engaged employees are essential to our continued success.


We are committed to fostering an environment where every employee feels valued and empowered to reach their full potential. As an essential partner in our shared success, you’ll benefit from inclusive development opportunities, flexible work-life support, paid volunteer days, and vibrant employee networks that keep you connected to what matters most. Join us in shaping the future.


As an Equal Opportunity Employer, we consider all qualified applicants for all positions without regard to race, creed, color, religion, national origin, ancestry, ethnicity, age, disability, genetic information, sex, sexual orientation, gender identity or expression, citizenship, marital status, domestic partnership or civil union status, familial status, military and veteran status, and other characteristics protected by applicable law.


Discover more information on jobs at StateStreet.com/careers


Read our CEO Statement

How we rate this

Vice President, Finance Data Product Management – P&L at State Street rates 31 out of 100 for how much of the daily work is AI. That makes it Little AI (AI Level 1 of 4). The level is about AI in the job, not seniority.

Classification

Little AI. AI is not part of the work.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Also common in finance jobs at this level

Pay

Typical finance job
$105k

This posting doesn't list pay. Use the typical finance figure when pay comes up. See finance jobs

Want an expert to read your CV for this job?

Free. Send your CV and the role you want next. You get your review by email within 24 hours.

Get new finance jobs by email

One email a week with the new finance jobs, each rated for how much AI is in the work. No recruiter spam, unsubscribe in one click.

Free. One email a week. Unsubscribe in one click.

Similar roles

Product roles that involve little AI, at other companies.

What kind of AI work fits you?

Answer 12 practical questions in about three minutes. Get a simple profile, the work it points to, and live roles to explore next.

Find my next step

More jobs at State Street

More finance jobs

Related searches

Same AI level

Jobs by city