Subway offers a comprehensive benefits package to support health, financial security, and work-life balance, including medical and life insurance, a retirement plan, a competitive bonus, mobility support, tuition reimbursement, paid holidays, and volunteer time off. The role emphasizes autonomous leadership, collaboration across teams, and a culture that values technical excellence, mentorship, and continuous learning.
About the role
The Sr Data Engineer is a senior, hands-on technical leader responsible for designing, building, and evolving Subway's enterprise data platform on Snowflake or Databricks, with onsite delivery in Shelton, CT. You will drive lakehouse architecture, engineering frameworks, and best practices across multiple data domains, delivering reference implementations, proofs of concept, and platform-level solutions with a high degree of autonomy.
Responsibilities
- Design and deliver reference implementations and production-grade frameworks on Databricks or Snowflake.
- Construct lakehouse platforms using Delta Lake or Iceberg with a Medallion Bronze/Silver/Gold pattern.
- Define and evolve enterprise data standards, reusable accelerators, and scalable patterns.
- Ensure solutions align with data governance, security, scalability, and cost-efficiency requirements.
- Benchmark technologies through hands-on evaluation rather than vendor presentations.
- Develop the first working versions of complex pipelines, frameworks, and POCs covering ingestion, CDC, streaming, data quality, observability, and CI/CD.
- Advance emerging technologies from POC to production, including Iceberg, Lakeflow, Openflow, Cortex, and Mosaic AI.
- Address performance, cost, and governance challenges at petabyte scale and mitigate systemic technical debt.
- Implement Lambda or Kappa architectures using Databricks Structured Streaming or Snowflake streaming options.
- Build GenAI and ML enablement patterns such as RAG, feature stores, and semantic layers with Databricks Mosaic AI or Snowflake Cortex.
- Partner with Data Science and Analytics teams to operationalize models and AI workflows.
- Collaborate with Product, Architecture, Security, Infrastructure, and Analytics leaders to translate business needs into technical direction.
- Communicate trade-offs, risks, and decisions clearly to technical and non-technical stakeholders.
- Contribute to roadmaps and platform investments by de-risking POCs through practical engineering insight.
- Mentor Senior and Staff Data Engineers through pair programming, PR reviews, and design coaching.
- Raise engineering maturity by shipping practical examples and codifying patterns.
- Foster a culture of technical excellence, learning, and continuous improvement.
Requirements
- Hands-on expertise with Databricks or Snowflake lakehouse platforms.
- Strong PySpark or advanced SQL skills, plus Python for data engineering and automation.
- Experience building Medallion architectures with Delta Lake or Iceberg tables.
- Real-time and batch streaming experience (Lambda or Kappa) with Databricks DLT or Snowflake Dynamic Tables / Snowpipe Streaming.
- Hands-on with orchestration tools such as Airflow, Databricks Lakeflow, or Snowflake Openflow; dbt experience is a plus.
- Solid data modeling capabilities (Dimensional, Data Vault, and schema design).
- Performance and cost optimization expertise (clustering, partitioning, Z-ordering, sizing, FinOps).
- Governance experience with Unity Catalog (Databricks) or Horizon Catalog (Snowflake) for lineage, access control, and data quality.
- Semantic layer experience with Databricks AI/BI Genie or Unity Catalog Metrics or Snowflake Semantic Views / Cortex Analyst.
- AI/ML enablement experience with Databricks Mosaic AI or Snowflake Cortex.
- CI/CD and DevOps fluency with Git, Databricks Asset Bundles or Snowflake CLI / Schemachange, and automated testing.
- Cloud platform experience across AWS, Azure, or GCP.
- Excellent communication and storytelling skills; comfortable navigating ambiguity and complex stakeholder environments.
- Education: Bachelor's degree in Computer Science, Engineering, or related field; advanced degree preferred.
- Experience: 3-5 years of professional data engineering with a proven track record leading architecture and hands-on build for enterprise-scale data platforms and influencing multiple teams without direct authority.
- Travel: Minimal to moderate, up to 10% as business needs require.
Technologies
- Databricks
- Snowflake
- Delta Lake
- Iceberg
- Medallion architecture
- PySpark
- SQL
- Python
- Databricks Structured Streaming / DLT
- Snowflake Dynamic Tables
- Snowpipe Streaming
- Airflow
- Databricks Lakeflow
- Snowflake Openflow
- dbt
- Unity Catalog
- Horizon Catalog
- Databricks Mosaic AI
- Snowflake Cortex
- Git
- Databricks Asset Bundles
- Snowflake CLI
- Schemachange
- AWS
- Azure
- GCP
- S3
- Glue
- Kinesis
Benefits
- Medical insurance
- Life insurance
- Retirement plan (401K/RSP)
- Bonus
- Mobility allowance
- Tuition reimbursement
- Paid holidays
- Volunteer time off
Compensation
The base salary range for this role is USD 102,700 to 128,400 per year. Final compensation will reflect a candidate's skills, experience, education, location, and internal equity.
