Director, Data Engineering

Data Platform Development

• Lead engineering design and delivery for the new data platform, including ingestion standards, Snowflake environment strategy, role-based access control, data zones/layers, metadata management, and service-level expectations.
• Create the enterprise ELT operating model: Drive adoption of Fivetran for managed ingestion, Snowflake for scalable storage and processing, and dbt for governed transformations, data quality tests, documentation, and reusable data models.
• Partner with data consumers to deliver curated, reusable data assets that support analytics, reporting, product experiences, client/advisor insights, operations, and future AI/ML workloads.
• Operationalize engineering quality: Embed automated testing, CI/CD, observability, cost monitoring, lineage, data contracts, and runbooks into platform delivery from the start rather than treating them as after-the-fact controls.

Migration Leadership and Delivery

• Own the migration roadmap: Lead planning and execution for migrating priority data domains, pipelines, models, and reporting dependencies from legacy platforms and bespoke integrations into the target Snowflake/Fivetran/dbt architecture.
• Manage cutover and coexistence: Define phased migration strategies, dependency maps, rollback plans, data validation routines, parallel-run approaches, and business-readiness checkpoints to minimize operational risk.
• Ensure migrated datasets meet clear acceptance criteria for completeness, accuracy, timeliness, lineage, access control, and auditability before decommissioning legacy assets.
• Coordinate cross-functional delivery: Orchestrate migration activities across Data Engineering, Application Engineering, Analytics, Data Governance, Security, Infrastructure, Product, and business owners; manage milestones, risks, issues, and executive communication.

Hands-On Engineering Oversight and Operational Excellence

• Stay technically engaged: Remain hands-on in architecture reviews, critical code reviews, data model reviews, and production-readiness decisions across Python, SQL, Snowflake, dbt, and orchestration patterns.
• Run the platform like a product: Own platform reliability, performance, SLAs/SLOs, incident response, capacity planning, Snowflake cost optimization, and continuous improvement of developer experience.
• Translate governance into controls: Implement data quality, lineage, classification, privacy, PII handling, audit trails, and access-control policies through automated engineering practices and platform guardrails.
• Support AI/ML readiness: Architect pipelines and curated feature-ready datasets that enable data science experimentation, model training, inference, and responsible AI governance.

Stakeholder Leadership and Team Development

• Lead and scale the team: Build, mentor, and develop a high-performing team of data engineers and architects, creating a culture of ownership, craftsmanship, technical rigor, and measurable delivery.
• Partner at the executive level: Communicate platform strategy, tradeoffs, migration progress, risks, and investment needs in a way that builds confidence with senior technology and business leaders.
• Own relationships with key platform vendors and data providers, including Snowflake, Fivetran, dbt, cloud providers, custodians, and other data ecosystem partners.
• Champion enterprise adoption: Help teams move from siloed data delivery to reusable, governed data products, creating clear intake, prioritization, support, and enablement models.