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.

Qualifications (some examples listed below):

• Strong experience leading data engineering teams and platforms in a technology-forward organization.
• Deep understanding of modern data architectures — cloud data platforms, batch/stream processing, lakehouse, and data mesh.
• Experience balancing delivery speed with governance, reliability, and scalability in complex environments.
• Ability to influence across Product, Analytics, Platform, and Security functions.
• Proven people leadership, delivery management, and vendor management skills.
• Strong communication skills and executive presence; comfortable presenting to C-suite stakeholders.
• Expert in SQL, Python, and PySpark; working knowledge of Scala is a plus.
• Proficient with orchestration tools (Databricks Workflows, Airflow, Azure Data Factory).
• Experience with data quality and observability tools (Great Expectations, Monte Carlo, Soda, or equivalent).
• Familiarity with dbt, Fivetran/Airbyte, or similar ELT frameworks.
• Working knowledge of Infrastructure-as-Code (Terraform, Pulumi, or ARM/Bicep).
• Bachelor’s degree required (Computer Science, Engineering, Data, Information Systems, or related field).
• Advanced degree (Master’s or MBA) preferred.
• 8–12 years of experience in data engineering or adjacent platform roles.
• 3–5 years of experience leading teams or enterprise-scale data capabilities.
• Demonstrated experience delivering production-grade data platforms on Databricks (strongly preferred), Snowflake, and/or Amazon Redshift.
• Experience operating cloud-based, distributed data platforms at enterprise scale.
• Experience in complex, matrixed enterprise environments — QSR, Retail, CPG, or Franchise industries preferred.

• Databricks Certified Data Engineer Professional or equivalent certification.
• Experience leading large-scale data platform migrations (e.g., Redshift to Databricks, on-prem to cloud).
• Exposure to ML/AI platform engineering — feature stores, model serving, and MLOps integration.
• Experience with FinOps practices for cloud cost optimization and chargeback models.
• Familiarity with Microsoft Purview, Collibra, or Alation for enterprise data governance.

What do we offer?

• Pension/401K/RSP (country specific)
• Competitive Bonus
• Mobility Allowance
• Tuition Reimbursement
• Company Holidays
• Volunteering time
• And More…..

The base pay range for this role is $184,500 - 230,600 annually

Pay within this range will be determined in good faith based on job-related factors, which may include skills, experience, education/training, location, and internal equity.