Senior-level data engineering leader responsible for designing, building, and evolving Subway’s enterprise data platform on Snowflake or Databricks, delivering reference implementations, POCs, and platform-level solutions across multiple data domains.
Responsibilities
- Design and build reference implementations and production-grade frameworks on Databricks or Snowflake.
- Architect lakehouse platforms using Delta Lake or Iceberg with a Medallion (Bronze/Silver/Gold) architecture.
- Define and evolve enterprise data standards, patterns, and reusable accelerators.
- Ensure solutions align with data governance, security, scalability, and cost-efficiency standards.
- Evaluate technologies through hands-on benchmarking instead of vendor decks.
- Develop the first working versions of complex pipelines, frameworks, and POCs (ingestion, CDC, streaming, DQ, observability, CI/CD).
- Advance emerging technologies (Iceberg, Lakeflow, Openflow, Cortex, Mosaic AI) from POC to production rollout.
- Address high-complexity performance, cost, and governance challenges at petabyte scale.
- Identify and mitigate systemic technical debt and architectural risks.
- Implement Lambda or Kappa architectures using Databricks Structured Streaming / DLT or Snowflake Dynamic Tables / Snowpipe Streaming.
- Build GenAI and ML enablement patterns (RAG, feature stores, semantic layers) using 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.
- Translate business needs into technical direction supported by working prototypes.
- Communicate technical trade-offs, risks, and decisions clearly to technical and non-technical stakeholders.
- Influence roadmaps and platform investments through technical insight and de-risking POCs.
- Mentor Senior and Staff Data Engineers through pair-programming, PR reviews, and design coaching.
- Elevate engineering maturity by shipping working examples and codifying patterns.
- Foster a culture of technical excellence, learning, and continuous improvement.
Requirements
- Location: Shelton, CT onsite.
- Education: Bachelor's degree required (Computer Science, Engineering, or related field); advanced degree preferred.
- Experience: 3-5 years of professional data engineering experience, with a proven track record leading architecture and hands-on builds for enterprise-scale data platforms.
- Exceptional hands-on expertise in Databricks or Snowflake lakehouse platforms.
- Deep proficiency in PySpark or advanced SQL, plus Python for data engineering and automation.
- Proven experience building Medallion architecture with Delta Lake or Iceberg Tables.
- Real-time and batch streaming experience (Lambda or Kappa) using Databricks DLT or Snowflake Dynamic Tables / Snowpipe Streaming.
- Hands-on with orchestration tools — Airflow, Databricks Lakeflow, or Snowflake Openflow; dbt experience a plus.
- Strong data modeling skills (Dimensional, Data Vault, schema design).
- Performance and cost tuning expertise (clustering, partitioning, Z-ordering, warehouse/cluster sizing, FinOps).
- Governance experience with Unity Catalog (Databricks) or Horizon Catalog (Snowflake) for lineage, access control, and data quality.
- Semantic layer experience using Databricks AI/BI Genie / 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 — Git, Databricks Asset Bundles or Snowflake CLI / Schemachange, automated testing.
- Cloud ecosystem experience — AWS (S3, Glue, Kinesis), Azure, or GCP.
- Excellent communication and technical storytelling abilities.
- Ability to operate in ambiguous and complex stakeholder environments.
- Travel: Minimal to moderate travel 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
About the Role
The Sr Data Engineer is a senior level, hands-on technical leader responsible for designing, building, and evolving Subway’s enterprise data platform on Snowflake or Databricks. This role serves as a technical authority and builder, driving Lakehouse architecture, engineering frameworks, and best practices across multiple data domains. The Sr Data Engineer operates with a high degree of autonomy, leading through working code and influence, shipping reference implementations, POCs, and platform level solutions.
What do we offer?
- Insurance Plans (Medical, Life)
- Pension/401K/RSP (country specific)
- Competitive Bonus
- Mobility Allowance
- Tuition Reimbursement
- Company Holidays
- Volunteering time
- And More…
Compensation
Base pay range: USD 102,700 - 128,400 per year. Pay within this range will be determined in good faith based on job-related factors, including skills, experience, education/training, location, and internal equity.
