Shelton - Data Engineering - Dir- Data Engineering

Own the design, development, and operations of Subway’s enterprise data engineering platform on AWS Databricks, including migration strategy from the existing Amazon Redshift environment.

Lead the delivery of Medallion Architecture (Bronze / Silver / Gold) pipelines across all QSR domains — Restaurant, Sales, Inventory, Guest, Marketing, and Supply Chain.

Oversee development of Delta Live Tables, batch and streaming pipelines, and feature engineering workflows across Databricks and Microsoft Fabric.

Architect and deliver a self-service data platform leveraging Unity Catalog, RBAC/ABAC governance, and Delta/Iceberg interoperability to enable domain teams to independently build and consume trusted data products.

Define and execute the platform migration roadmap — phasing workloads from Redshift to Databricks/Snowflake with minimal business disruption.

Partner with the Enterprise Data & AI Architect to implement and evolve a Data Mesh architecture, enabling domain teams to own and publish certified data products.

Drive adoption of DataOps practices including CI/CD for data pipelines, automated testing, data contract enforcement, and pipeline observability.

Ensure platform reliability, SLA adherence, and proactive incident management for all production data pipelines and data products.

Implement infrastructure-as-code and environment management for Databricks workspaces, clusters, and job orchestration.

Establish on-call processes, runbooks, and escalation paths for Tier-1 data platform incidents, targeting ≤99.5% pipeline uptime.

Define and enforce data quality standards and SLAs across all engineering pipelines and analytics products — partnering with the Data Governance function and Data Product Owners.

Implement data observability frameworks (e.g., Monte Carlo, Databricks Lakehouse Monitoring) to proactively detect and resolve data freshness, completeness, and accuracy issues before they impact business decisions.

Partner with the Data Governance team to ensure pipelines and data products are cataloged, lineage-tracked, and classified in Unity Catalog and/or Microsoft Purview.

Champion metadata management and data contract enforcement as first-class engineering practices across the platform.

Collaborate with Data Product Managers to translate business outcomes into engineering priorities and delivery plans.

Enable Analytics, BI, and Data Science teams with high-quality, well-modeled data assets that accelerate insight generation.

Communicate platform health, tradeoffs, and delivery status to business and technology stakeholders through regular forums and reporting.

Serve as the primary data engineering partner to product and domain leaders — translating business needs into engineered, scalable data solutions.

Lead vendor relationships and participate in technology evaluations, RFPs, and contract negotiations for data platform tooling and services (Databricks, Snowflake, dbt, Fivetran, etc.).

Manage the data engineering budget — including cloud infrastructure costs (AWS, Azure), tooling licenses, DBU consumption, and contractor spend.

Develop and present quarterly technology roadmaps to senior leadership, aligning platform investments to enterprise data and AI strategy.

Collaborate cross-functionally with Application Engineering, InfoSec, and Enterprise Architecture to ensure platform alignment with enterprise standards.

Lead and develop managers, leads, and senior data engineers across onshore and offshore delivery models.

Set clear goals, performance expectations, and delivery standards aligned to organizational OKRs.

Build strong engineering capability through hiring, coaching, and career development — scaling the team to 10–20+ engineers (FTEs and contractors).

Establish engineering standards, code review practices, and a culture of continuous improvement and technical excellence.

Foster a culture of ownership, reliability, and continuous improvement within the data engineering function.

Define and track KPIs including pipeline reliability, data freshness, SLA adherence, and cost efficiency.

Drive operational excellence, automation, and cost optimization across all data platforms and infrastructure.

Support incident analysis and preventative improvements to continuously raise the reliability bar.