Director Data Engineering

Director of Data Engineering | Snowflake, dbt & AI-Ready Data Platforms

The Talent Source is partnering with a rapidly growing, private equity-backed national services organization to identify a Director of Data Engineering who can build the data foundation for its next phase of growth.

This is not a traditional reporting or analytics leadership role.

We are looking for a technically deep, hands-on Data Engineering leader who can architect and evolve a modern Snowflake-native data platform , transforming fragmented data from multiple businesses and applications into governed, connected and AI-ready data assets.

The organization has grown rapidly through acquisition, creating an exciting opportunity to establish a common data foundation across multiple ERP, operational and SaaS platforms without waiting for wholesale application consolidation.

The Director will own the data supply chain from source connectivity and ingestion through dbt transformation, semantic modeling and AI enablement , creating the trusted data foundation for enterprise analytics, intelligent applications and emerging AI use cases.

What You'll Build

You will lead the continued evolution of a platform centered on Snowflake, dbt, AWS and modern ELT/ETL technologies , while helping shape how emerging Snowflake AI capabilities are applied across the enterprise.

Key priorities include:

• Snowflake Architecture: Own and evolve the enterprise Snowflake platform, including data modeling, performance, security, scalability and cost optimization.
• dbt & Semantic Layer: Build a governed, version-controlled semantic layer that establishes consistent enterprise metrics, dimensions, entities and relationships across the business.
• AI-Ready Data Foundation: Structure enterprise data so it can be reliably consumed not only by Power BI and analytics teams, but also by AI agents, natural-language interfaces and intelligent applications .
• Snowflake Cortex AI: Explore and implement capabilities including Cortex Analyst, Cortex Search and Cortex ML , enabling natural-language access, semantic search and AI-generated signals from enterprise data.
• Knowledge & Entity Modeling: Help establish a connected view of customers, projects, assets, suppliers, products and employees, moving beyond isolated tables toward a richer understanding of relationships across the enterprise.
• Data Connectivity: Integrate data from multiple ERP, SaaS, operational and third-party sources using the right ingestion approach for each use case, including Matillion, Snowpipe and custom pipelines.
• Data Quality & Observability: Establish automated testing, monitoring, lineage, data contracts and SLAs that ensure downstream users and AI applications can trust the data.
• DataOps: Drive code-first engineering practices including Git, CI/CD, automated testing and controlled environment promotion.
• Team Leadership: Build and develop the Data Engineering function while directing third-party development resources and remaining technically engaged as a player-coach.

The internal mandate specifically calls for a semantic layer that can serve Power BI, Snowflake Cortex and downstream AI agents , with dbt providing the authoritative, version-controlled business definitions.

What We're Looking For

We are particularly interested in leaders who bring:

• 8–10+ years of Data Engineering experience , including leadership or principal-level responsibility.
• Deep, hands-on Snowflake experience in production environments, with the ability to discuss specific architectural decisions, performance challenges and cost optimization.
• Strong dbt Core and/or dbt Cloud expertise, including modular model design, testing, documentation, CI/CD and ideally semantic-layer capabilities.
• Advanced SQL and working Python proficiency.
• Experience designing enterprise data models and semantic layers that create consistent definitions across disparate source systems.
• Understanding of knowledge graphs, ontologies, entity relationships or similar approaches to connected enterprise data .
• Practical exposure to AI-enabled data architectures , ideally including Snowflake Cortex, RAG, vector embeddings, semantic search or LLM integration.
• Experience building highly reliable data platforms with automated testing, observability, lineage and data contracts.
• Strong AWS data-platform knowledge.
• Experience with modern ELT/ETL architectures such as Matillion
• Experience supporting high-growth, acquisitive, private equity-backed, construction, industrial, distribution or similarly complex organizations is highly desirable.

The Leader We're Looking For

This role requires more than someone who has managed Data Engineering teams.

We're looking for a builder who remains close to the technology, thinks about data as connected business knowledge rather than simply tables and pipelines, and understands that tomorrow's enterprise data platform must be designed for both human and AI consumption .

You should be equally comfortable discussing a Snowflake architecture with engineers, working through business definitions with Finance and Operations, reviewing a dbt model, and exploring how Cortex or an AI agent could unlock new value from the data foundation you've built.

The organization is looking for someone who combines a code-first engineering mindset with pragmatic architecture and strong business judgment; someone who can establish the right long-term foundation while continuing to deliver value incrementally.

Location: Flexible / Remote with travel as required

Compensation: Competitive executive-level base salary + annual incentive

If you are a hands-on Data Engineering leader who has built modern Snowflake platforms and is excited by the opportunity to create the semantic and knowledge foundation for enterprise AI , we'd welcome the opportunity to connect.

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