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AI, Data & Research - Data & Analytics

Sr. Director, Data Platform

Director / VP / Exec

Independently audited

Turns the company's data into decisions people actually trust. Owns the hardest problems in the area and raises the standard of everyone around them.

Audited package

Built, independently audited role package. Everything on this page is written for this seat.

Your Stack (Swappable)

This role works against whatever tools you already run. We list the tool categories the role operates in, never a named vendor, so you are never asked to replace a system that works.

Data Warehouse & Lakehouse
Data Pipeline (ETL/ELT)
ML / feature platform
Observability & Monitoring
Governance / catalog control plane
FinOps / cost
Roadmapping & Portfolio

Deliverables

Dashboards and reports
Data models and documentation
Analysis write-ups
Data quality standards

Responsibilities

Own the data platform's strategy and target-state (reference) architecture at org scale - the warehouse/lakehouse, ingestion/orchestration, transformation/semantic tooling, the ML/feature platform, observability, and the governance control plane - and the multi-team roadmap that gets there
Make and defend the recurring build-vs-buy and major-vendor decisions for the data stack within the delegated envelope; manage lock-in, TCO, and migration risk deliberately
Set the platform's reliability commitments - freshness and availability SLAs/SLOs, the tiering behind them, and how blast radius is contained - and own the response when the foundation itself fails
Own the cost of the data stack (FinOps): the unit economics of compute and storage, the cost trajectory vs plan, showback/chargeback that makes platform cost legible, and the calls that keep it affordable without breaking consumers
Own the platform as a product: the paved road / self-serve capabilities, the interfaces and contracts the platform commits to its consumers, and the adoption of the golden path over bespoke per-team builds
Lead and develop the platform-engineering managers and leads - hiring, performance, leveling, and succession for the platform org's leadership bench
Set and sequence the platform roadmap by leverage-per-unit-of-capacity and by dependency - the foundational work that unblocks every consuming team first, not the shiniest single component
Build and operate the governance/security tooling (catalog, lineage, access control plane, policy-as-code, data-quality/observability) that makes the function's governance standard enforceable and observable at scale

The essence of this seat

The Sr.

Signature move

Start from the internal customer, not the component. The first move on any platform request is to name which consuming teams it serves and what class of work it removes for them - a request that helps one team but does not raise the foundation's leverage is the signal to reframe, not to build

What good looks like

converts a mandate into a target-state reference architecture plus a paved

Decision rights

- the platform strategy and the target-state architecture within the VP's mandate, the platform roadmap and its sequencing, build-vs-buy and major platform-tooling selection within your budget envelope, the platform's SLAs/SLOs and reliability targets and the tiering behind them, the platform org design and manager assignments, performance and leveling for your platform managers and leads, the FinOps/cost posture and how platform capacity and budget are allocated across your teams, and risk acceptance for platform reliability and cost to your delegated threshold

Working vocabulary

platform-as-product / internal customerspaved road / golden path / self-servetarget-state (reference) architectureSLA / SLO / error budget / freshness SLAcapacity / concurrency / autoscalinginterface contract the platform commitsleader of leaders / span of control. ---

What this role does

The Sr. Director of Data Platform owns the data-platform organization - the shared foundation every other data and ML team builds on: the warehouse or lakehouse and its table format, the ingestion and orchestration control plane, the transformation and semantic/metrics tooling, the ML and feature platform, the data-reliability and observability layer, the governance and access control plane, and the cost of that whole stack. They report to a VP of Data (or a CDAO / Head of Data) and are accountable for what the platform delivers to the teams that run on it: a foundation that is reliable, fast to build on, governable, and affordable. Where a Data Director owns the outcomes of the broader function, this seat owns the platform others build on and answers for its architecture, reliability, and cost. The distinguishing frame is that the platform is a product, and its customers are internal - the analytics engineers, data engineers, data scientists, and ML engineers who ship on it. So the seat is not measured in features shipped or tickets closed; it is measured in leverage (a class of work removed from every consuming team at once), reliability (the SLA the platform keeps to everyone who depends on it), and unit cost (what a query, a pipeline, or a served feature actually costs). The leader sets the target-state architecture and the paved road, makes the recurring build-vs-buy call at org scale, and arbitrates the deep technical direction - but does not do the IC build: writing the pipeline, tuning the warehouse, or standing up the serving path is the Data Engineer's and MLOps Engineer's craft, a tier down. Doing it means the platform org has lost its architect and reliability owner to help one team ship one thing. What separates great from average is platform-as-product judgment plus architecture and cost that compound. An average platform director runs the stack as an infrastructure cost center - chasing the newest tooling, building whatever the loudest team demands or whatever is interesting, reporting uptime and ticket counts while the cloud bill and the bespoke-integration sprawl quietly grow. A great one treats the internal teams as customers, invests where it raises every team's leverage at once (a golden path that retires a class of glue code, a reliability guarantee teams can plan against, a self-serve capability that removes a queue), makes build-vs-buy and architecture bets that survive three years and avoid lock-in nobody chose, holds reliability and cost as first-class commitments rather than afterthoughts, and can show the platform's leverage and unit economics - not just that the lights are on.

Best-fit customer

teams whose reporting is not yet trusted enough to decide from

Need more than one role?

Buying the whole team or the complete department lowers your effective cost per role at every tier of the ladder.

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