Nidhi Sharma

This was my longest early run — about four years with Adobe, through TCS, during their move to a subscription model. I was hands-on building the ETL and data pipelines behind that transition, and I led an offshore scrum team supporting Project Hendrix, their internal CRM tool.

The hard part was never the code — it was coordinating an offshore engineering team against product and architecture in a different timezone, on a business that was changing underneath us. So I ran the scrum ceremonies, kept the backlog honest, and made sure dependencies between the CRM work and the pipeline work surfaced early, not at release.

Working across so many client engagements taught me to adapt fast — new stacks, new teams, new expectations. And because I was writing the SQL and building the pipelines myself, I learned where data actually breaks and what to ask — which is still how I approach any new program.

At Cohesity, pre-IPO, I led a cloud data-warehouse migration — moving their sales and renewal revenue data off SQL Server onto Snowflake. The risk with revenue data is obvious: if the numbers don’t match after the move, finance stops trusting the whole platform.

So I ran both systems in parallel for four weeks and validated daily. Early on, source and target were off by around thirty percent, and I built a three-tier set of checks that drove that to exact parity. One gap turned out to be a timezone mismatch between the old servers and the cloud that was throwing off trailing-twelve-month renewals — I traced it to the transformation layer and fixed it upstream.

We cut over on schedule with the financials matching to the penny, zero downtime. The part I’m proudest of isn’t the cutover — it’s that I caught a silent data bug before it ever reached a finance dashboard.

At Palo Alto Networks I owned a buyer-intent data pipeline across five concurrent workstreams. Enterprise security deals involve a lot of people over a long cycle, and the signals were scattered — third-party intent data in one place, first-party web behavior in another, nothing tied to an account. I built the pipeline that pulled those together, ran entity resolution to roll everything up to real accounts, and pushed the result into the CRM.

Two things I’d point to. First, an upstream vendor kept changing their data format and breaking us, so I enforced strict schema contracts and validation at ingestion — we got to sixty-four of sixty-five daily runs landing on time. Second, we shipped a dashboard the reps wouldn’t touch, so I stopped fighting it and moved the intelligence into the CRM they already lived in — weekly active reps went from eleven to thirty-eight.

That’s the pattern I care about: find the dependency or the friction that’s actually blocking adoption, and fix that one thing.

At NetApp I’m in the partner and channel space, and I run two things in parallel. One is a vendor data-warehouse migration — managing the integration and the source-to-target mapping as we move off one vendor’s platform. The other is partner-marketing attribution, the MDF program — figuring out what every partner-marketing dollar actually sourced.

That’s a messy data-hygiene problem: partner and customer names come in free-text, multi-language, across marketing, CRM, and ERP, and none of it joins cleanly. I designed the matching logic — fuzzy matching with confidence thresholds — that resolved hundreds of thousands of records to real accounts, with a human-review queue for the ones the system wasn’t sure about, so a bad match never drove a payout.

Day to day I’m gathering stakeholder requirements, owning the roadmaps, and keeping partner ops, data engineering, and marketing aligned. And I lean on my own AI workflows to compress the front end — turning thirty-plus stakeholder interviews into a prioritized roadmap in days.