Director, Data Platform

The Director, Data Platform will build and run AHEAD’s Data Platform Team— a single, unified group accountable for every governed connection the enterprise exposes into its data domains. This is a build-and-run leadership role: the Director will stand up the team, the platform, and the operating model simultaneously, then own it on an ongoing basis.

The platform model centers on one governed connection per data domain — exposed as an MCP server, API, direct SQL connection, or event stream — with two backend lanes behind every connection: a read lane serving curated, AI- and analytics-ready data products, and an operational lane serving approved enterprise actions and event-driven integration back into source systems. The Director owns both lanes, the connection layer that unifies them, the access model that determines what any caller — person, application, AI agent, or integration pipeline — is permitted to do, and the event streaming infrastructure that powers domain-event-driven consumers such as Hatch.

This is a hands-on architectural and operational leadership role. The Director will be the senior-most technical authority on Snowflake, data governance, platform access and authorization, FinOps, ingestion, medallion architecture, data product publishing, and event 

The Director owns seven core pillars of the Data Platform Team, plus the event streaming and messaging infrastructure that is essential for integration-pipeline and event-driven consumers:

1.  Snowflake Ownership

Own the target-state Snowflake architecture: data organization, storage patterns, warehouse and workload segmentation, performance optimization, and lifecycle management.

Set platform-wide standards for environments, security model, scalability, and operational resilience.

Serve as the senior-most technical authority on Snowflake within the enterprise, with deep hands-on credibility.

2.  Data Governance

Own the enterprise data governance vision, operating model, and leadership cadence — embedded into platform delivery and operations, not run as a disconnected compliance function.

Lead the governance agenda across ownership, stewardship, quality, metadata, lineage, cataloging, retention, classification, and policy enforcement.

Hire and lead a Data Governance leader reporting into this role, while retaining accountability for enterprise data policy, standards, and outcomes.

Design governance controls that support AI and agent-based consumption: policy-aware access, auditability of actions, appropriate use of sensitive data, and traceability of context.

3.  Data Platform Auth, Use Cases, and Access

Own the access-rights model that governs every domain connection: who can read, who can write, who can subscribe to event streams, and under what scope — by role, by application, and by AI agent.

Define and enforce authentication and authorization standards at the connection layer, ensuring every call is identified, scoped, and audited regardless of which backend lane serves it.

Own the catalog of domain connections and the self-service model that lets consumers discover what they can access and request what they cannot.

Partner with Security to ensure access governance, policy enforcement, and use-case approval processes scale with the number of domain connections and consumers.

4.  FinOps

Own platform cost governance for Snowflake and the surrounding data estate: budgeting, spend visibility, showback/chargeback policy, and optimization guardrails.

Establish unit economics and cost-per-workload visibility so platform investment decisions are made with full cost transparency.

Drive continuous cost optimization without compromising performance, reliability, or governance standards.

5.  Ingestion

Own the standards and patterns for data ingestion into the platform: batch, CDC, streaming, and event-driven sources.

Define source onboarding standards, schema change handling, and replay/backfill patterns.

Ensure ingestion patterns scale cleanly as new domains and source systems are added to the platform.

6.  Medallion Architecture

Own the Bronze → Silver → Gold processing model: raw retention, cleansing and conformance, and product-ready modeling.

Set transformation standards, testing practices, and reconciliation processes across all medallion layers.

Ensure the medallion architecture is the consistent foundation underneath every domain connection’s read lane, regardless of who contributed the underlying product.

7.  Data Product Publishing and Documentation

Own the end-to-end lifecycle for data products: intake, build, review, publication, change control, and retirement.

Establish documentation standards so every published product has a clear owner, contract, SLA, classification, and lineage.

Build and run the federated contribution model: approved domain teams can build and submit read-side data products; the core team reviews, scales, and promotes them into the shared catalog while retaining final editorial control over what gets published.

Drive platform-wide discoverability and self-service consumption so published products are easy to find, understand, and adopt.

8.  Event Streaming and Messaging Infrastructure

In addition to the seven pillars above, the Director owns the event streaming and messaging layer that powers integration-pipeline and event-driven consumers:

Own the domain event streaming infrastructure — the platform through which consumers subscribe to governed domain events rather than polling source systems directly. Current infrastructure includes MuleSoft

Establish event contracts as a first-class element of every domain connection: schemas, topic naming, retention policies, ordering guarantees, and access rights are governed at the connection level

Ensure event streams feed the read lane’s ingestion pipelines as a governed push-based delivery mechanism into the medallion stack

Partner with integration platform owners and application teams to migrate from point-to-point event wiring toward governed, domain-aligned event contracts under this team’s stewardship.

Build the Data Platform Team : recruit, structure, and lead a high-performing organization spanning platform engineering, data governance, data product delivery, and event infrastructure.

Define and own the architecture for unified domain connections — one governed connection per data domain, exposing curated reads, operational reads, governed write actions, and event stream subscriptions through a single contract consumers integrate to.

Design the platform to support both analytical and operational use cases, enabling trusted historical insight while supporting the real-time, entity-specific access patterns required for AI agents, automated workflows, and event-driven integration pipelines.

Architect governed write-back patterns and event emission patterns that allow downstream systems, workflows, or AI agents to act safely on platform data, with full auditability and scoped access rights.

Partner with Engineering, Architecture, Product, and Security to ensure the platform is resilient, trusted, extensible, and aligned to enterprise priorities.

Build a platform operating model that balances central ownership with federated, domain-aligned contribution — enabling teams to move quickly without compromising governance or architectural integrity.

Establish platform engineering standards for performance, resilience, monitoring, incident response, disaster recovery, and service-level expectations across all connection modes: direct SQL, API, MCP, and event stream.

Own the platform roadmap across foundational build-out, Snowflake optimization, governance maturity, event streaming infrastructure, and future-facing capabilities that support AI-driven workflows.

Provide strong cross-functional leadership and executive communication, translating platform decisions into business impact, delivery tradeoffs, and investment priorities.

Bachelor’s degree or equivalent experience.

12 or more years of experience in data engineering, platform engineering, data architecture, or related technology roles.

At least 5 years in a leadership role with responsibility for platform strategy, architecture, and engineering team leadership — including building a team or function from the ground up.

Demonstrated experience architecting and operating modern enterprise data platforms in cloud environments.

Strong hands-on experience with Snowflake, including platform design, performance optimization, security, workload management, and FinOps/cost governance.

Experience designing and running data governance programs: ownership, stewardship, quality, metadata, lineage, cataloging, classification, and policy enforcement.

Experience designing access and authorization models for data platforms, including role-based, application-based, and AI-agent access patterns.

Experience designing data ingestion patterns across batch, CDC, and streaming sources, and medallion-style (Bronze/Silver/Gold) processing architectures.

Experience with event streaming and messaging infrastructure — e.g. Kafka, MuleSoft, or equivalent — including event schema governance, topic design, retention policy, and integration with data platform ingestion pipelines.

Experience building data product publishing programs, including documentation standards, catalog design, and self-service consumption models.

Experience building or enabling platforms that support AI, intelligent automation, or agent-based workflows.

Experience designing governed write-back or operational integration patterns that allow downstream systems, workflows, or agents to act safely on platform data.

Strong executive communication and stakeholder management skills, with the ability to align technical architecture decisions to business value.