FinOps Platform Engineering, Sr. Manager

Location:

This is a hybrid remote/in-office role, Iselin, NJ

Medidata follows a hybrid office policy in which employees who are hired for an in-person position are expected to work on site a certain number of days per week following Company policy.

Medidata is powering smarter treatments and healthier people through digital solutions to support clinical trials. Celebrating over 25 years of ground-breaking technological innovation across more than 38,000 trials and 12 million patients, Medidata offers industry-leading expertise, analytics-powered insights, and one of the largest clinical trial data sets in the industry. More than 1 million registered users across approximately 2,300 customers trust Medidata's seamless, end-to-end platform to improve patient experiences, accelerate clinical breakthroughs, and bring therapies to market faster.

A Dassault Systèmes brand (Euronext Paris: FR0014003TT8, DSY.PA), Medidata is headquartered in New York City and has been recognized as a Leader by Everest Group and IDC. Discover more at  Listen to our latest podcast, from Dreamers to Disruptors, and follow us at @Medidata.

Fin Ops operates like a product team: the business — Engineering, Product, Finance, Procurement, and Operations — is our customer, and cost/usage insight is the product. The Fin Ops Platform Engineering, Sr. Manager is the technical builder who makes that possible: this role owns the tooling, integrations, and automation that let Fin Ops operate as an autonomous, standalone unit rather than a group of people manually pulling numbers on request.

This is a hands-on engineering role at the Sr. Manager level. You will design, code, and ship the Fin Ops platform yourself — the title reflects seniority and scope of ownership, not a coordination or people-management function.

• Design, build, and maintain the tooling and integrations that connect cost and usage data sources — AWS, Snowflake, and HDC (our clinical data cloud) — into a unified Fin Ops platform.
• Build automated tagging enforcement and validation pipelines against the framework defined by the Fin Ops Architect, so compliance is built in at deploy time, not checked after the fact.
• Build self-service tools and APIs that let Engineering, Product, and Finance pull their own cost and usage insights on demand — treating internal stakeholders as customers of a product, not requesters in a queue.
• Own the data pipelines feeding executive and team-level dashboards (Thought Spot, Unified Portal), including reconciliation logic that ties attributed and unattributed spend back to the invoice — automating what would otherwise require manual analysis.
• Automate cost allocation, showback/chargeback calculations, and unit-economics computations (e.g., cost per trial, cost per environment).
• Instrument and meter AI/LLM usage and spend across providers — token-level telemetry, attribution to teams and workloads, and integration with gateway and measurement tooling — so AI cost is as visible and governable as cloud cost.
• Build integrations with vendor and procurement systems to automate renewal tracking, contract data ingestion, and scorecard generation.
• Build anomaly detection and automated alerting on cost and usage data, closing the gap between a monthly report and a real-time signal.
• Build automation to audit and optimize Savings Plan / Reserved Instance coverage, rather than tracking it manually.
• Partner with Engineering to instrument systems for cost and usage telemetry at the source, including detection of cost regressions introduced by code and configuration changes.
• Own the technical roadmap for Fin Ops tooling, including build-vs-buy decisions on platform components.
• Apply standard engineering rigor…