2021 – 2024 · Bengaluru, India
ThreeyearsatPwC.Threeindustries.Threeexecutivesponsors.
Clients across the globe. Private equity in New York, FMCG in London, banking in the Caribbean. Names hidden for NDA-compliance.
- Period
- Nov 2022 – Aug 2024
- Location
- New York (PwC India delivery)
- Client scale
- $600B+ AUM private equity
- Role
- Core member, Global Data Operations & Digital Transformation
Private Equity Client, New York ($600B+ AUM)
Owned the rollout of an AI-enabled portfolio monitoring platform across a 150+ company portfolio.
The client runs one of the largest private equity portfolios in the world. The data operation behind that portfolio — gathering financials, valuations, ESG metrics, regulatory filings from 150+ companies and surfacing them to partners and analysts — was manual, slow, and prone to errors. PwC was brought in to design and roll out a new AI-enabled, cloud-first SaaS platform that would handle the full data lifecycle.
I owned end-to-end product delivery on that rollout. Vendor evaluation. Cost-benefit modeling. Requirements definition. Discovery interviews with 50+ front- and middle-office users. PRDs. A prioritized backlog for the vendor. A firm-wide adoption program that ran for four months and reached 70+ stakeholders. And a four-person workstream I led across the Americas PE, Growth PE, and Impact PE strategies, with individual contribution to the Europe PE strategy.
What I owned
- End-to-end product rollout across 150+ portfolio companies and 200+ users
- Vendor evaluation and cost-benefit modeling that informed the platform build
- Discovery and feedback cycles with 50+ front- and middle-office users
- A four-person workstream across three PE strategies
- Firm-wide adoption program: gamified trainings, A/B-tested workflow variants, roadshow materials
What it shipped
- Reduced manual financial data review effort by 60%
- Cut new-investment onboarding-to-reporting cycle by 75%
- Unlocked ~$350K/yr in projected operational savings
- Reduced financial data review downtime by 91% and DQ issues by 80%
- Improved regulatory reporting accuracy by 25% and cut submission delays by 30% (ESG, Anti-trust, SEC Form PF)
- Accelerated reporting timelines by three weeks per cycle for LP/GP one-pagers and quarterly reports
- Lifted platform utilization and cut support tickets through the adoption program
- Period
- May 2022 – Oct 2022
- Location
- London (PwC India delivery)
- Client scale
- Global Top 5 FMCG by revenue
- Role
- Consultant, Central Data Quality
FMCG / CPG Client, London (Global Top 5 by Revenue)
Data Quality lead for the client's Global R&D function. Five regions. 150+ critical data assets.
The client's Global R&D function runs on data — formulations, ingredient databases, regulatory submissions, supplier specs, market launches across 5 regions. When that data drifts, products ship late and marketing spend lands on the wrong SKUs. The Central Data Quality team's job was to make the data dependable. Mine was to lead Data Quality operations for the R&D arm of that program.
The work spanned three threads. First, observability — operational dashboards in PowerBI tracking field-level data quality metrics, pipeline performance, and Azure consumption. Second, automation — validation checks across data pipelines built in collaboration with engineering teams. Third, master data — standardization across five global markets. Each thread had measurable outcomes, but the deeper lesson was about coordinating Agile delivery across geographies where product launches couldn't wait for data to be perfect.
What I owned
- Data Quality lead for the client's Global R&D function
- Agile delivery coordination with engineering teams across 5 global regions
- Prioritization of data governance requirements for 150+ critical R&D data assets
- Operational dashboard design in PowerBI
What it shipped
- Reduced cloud spend by €4K/month by identifying redundant workloads through dashboard analysis
- Cut manual reconciliation by 72 analyst-hours/month and lowered validation errors by 80%
- Standardized master data across 5 global markets
- Cut data delays by 50% and accelerated GTM by 2 weeks in high-growth regions
- Improved product performance visibility by 20% and enabled better marketing allocation for high-demand SKUs
- Period
- Sep 2021 – Apr 2022
- Location
- Caribbean (regional)
- Client scale
- Leading Caribbean banking firm
- Role
- Core member, Digital Transformation
Caribbean Banking Giant
Reported direct to the CIO on a regulated banking data strategy.
The client is one of the leading banking firms in the Caribbean. Like most regulated banks, it sits under BCBS (Basel Committee on Banking Supervision) standards, which means its data has to be auditable, lineage-tracked, and provably correct. The bank wanted to overhaul its data quality strategy and pick a remediation tooling vendor for the next phase. PwC was brought in to design the strategy. I was on the core team.
The engagement reported direct to the Chief Information Officer. It was the first time I'd worked at that level of seniority, and the first time I'd seen up close how a regulated bank's executive committee actually thinks about data risk. Most of the work was discovery — workshops, interviews, mapping critical data elements, building rules. The output was a Data Quality Index that surfaced ~80% of critical data gaps and a vendor recommendation that saved the bank significant licensing cost.
What I owned
- Workshop facilitation across IT, the executive committee, and business analysts
- Mapping of 150+ Critical Data Elements (CDEs) aligned with BCBS regulatory standards
- Design of initial Data Quality standards for the compliant framework
- Construction of the Data Quality Index across enterprise datasets
- Competitive vendor evaluation and cost-benefit modeling
What it shipped
- Maximized data quality standards compliance by 95%
- 10+ cross-functional workshops with IT, executives, and analysts
- Authored business rules aligned with BCBS regulatory standards
- Drill-down DQ reports using SQL and Excel defining DQ index and impact
- Surfaced ~80% of critical data gaps across enterprise datasets
- 27% reduction in DQ issues across critical data tables through profiling, cleansing, and monitoring
- Unlocked US$211K in avoided licensing and integration costs through vendor evaluation
- Delivered a phased implementation roadmap to the CIO
What three engagements taught me.
Three industries in three years sounds like breadth on a resume. The actual lesson was different. Every engagement looked like a data problem on the surface and turned out to be a stakeholder problem underneath. The PE partners didn't want better dashboards — they wanted to trust the numbers. The R&D scientists at the FMCG client didn't want stricter validation rules — they wanted GTM not to slip. The bank's executive committee didn't want a vendor — they wanted a defensible answer for the regulator.
The work that shipped was the work that figured out which problem the client actually had. That's the muscle I brought into product management — and the muscle I'm still building, now with AI as the lever instead of data governance.
Other roles
Around PwC
CareGiver24
Sep 2024 – Apr 2025
Product role after PwC — caregiver-matching marketplace serving home-care families.
AASPE Technologies
Jan 2021 – Jun 2021
Engineering and product on enterprise web and ETL tooling.
NIPFP (National Institute of Public Finance & Policy)
Winter 2018 (internship)
Research engineering for public finance datasets and economic indicator pipelines.