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 that other teams build upon. Responsibilities include but not limited to: Personally design and build reference implementations and production-grade frameworks on Databricks or Snowflake.\nDesign lakehouse platforms using Delta Lake or Iceberg Tables with Medallion (Bronze/Silver/Gold) architecture.\nDefine and evolve enterprise data standards, patterns, and reusable accelerators.\nEnsure solutions align with data governance, security, scalability, and cost-efficiency standards.\nEvaluate technologies through hands-on benchmarking - not vendor decks.\nBuild the first working version of complex pipelines, frameworks, and POCs (ingestion, CDC, streaming, DQ, observability, CI/CD).\nDrive emerging tech (Iceberg, Lakeflow, Openflow, Cortex, Mosaic AI) from POC to production rollout.\nSolve high-complexity performance, cost, and governance challenges at petabyte scale.\nIdentify and address systemic technical debt and architectural risks.\nImplement Lambda or Kappa architectures using Databricks Structured Streaming / DLT or Snowflake Dynamic Tables / Snowpipe Streaming.\nBuild GenAI and ML enablement patterns (RAG, feature stores, semantic layers) using Databricks Mosaic AI or Snowflake Cortex.\nPartner with Data Science and Analytics teams to operationalize models and AI workflows.\nCollaborate closely with Product, Architecture, Security, Infrastructure, and Analytics leaders.\nTranslate business needs into sound technical direction backed by working prototypes.\nCommunicate technical trade-offs, risks, and decisions clearly to technical and non-technical stakeholders.\nInfluence roadmaps and platform investments through technical insight and de-risking POCs.\nMentor Senior and Staff Data Engineers through pair-programming, PR reviews, and design coaching.\nRaise engineering maturity by shipping working examples and codifying patterns.\nFoster a culture of technical excellence, learning, and continuous improvement. Qualifications (some examples listed below): Exceptional hands-on expertise in Databricks or Snowflake lakehouse platforms.\nDeep proficiency in PySpark or advanced SQL, plus Python for data engineering and automation.\nProven experience building Medallion architecture with Delta Lake or Iceberg Tables.\nReal-time and batch streaming experience (Lambda or Kappa) using Databricks DLT or Snowflake Dynamic Tables / Snowpipe Streaming.\nHands-on with orchestration tools - Airflow, Databricks Lakeflow, or Snowflake Openflow; dbt experience a plus.\nStrong data modeling skills (Dimensional, Data Vault, schema design).\nPerformance and cost tuning expertise (clustering, partitioning, Z-ordering, warehouse/cluster sizing, FinOps).\nGovernance experience with Unity Catalog (Databricks) or Horizon Catalog (Snowflake) for lineage, access control, and data quality.\nSemantic layer experience using Databricks AI/BI Genie / Unity Catalog Metrics or Snowflake Semantic Views / Cortex Analyst.\nAI/ML enablement experience with Databricks Mosaic AI or Snowflake Cortex.\nCI/CD and DevOps fluency - Git, Databricks Asset Bundles or Snowflake CLI / Schemachange, automated testing.\nCloud ecosystem expertise - AWS (S3, Glue, Kinesis), Azure, or GCP.\nExcellent communication and technical storytelling ability.\nComfortable operating across ambiguity and complex stakeholder environments.\n

Education:

Bachelor's degree required (Computer Science, Engineering, or related field); advanced degree preferred.\n

Experience:

3-5 years of professional data engineering experience, with proven track record leading architecture and hands-on build for enterprise-scale data platforms, operating in complex or mission-critical data environments, and influencing multiple teams and platforms without direct authority.\n

Travel Requirements:

Minimal to moderate (up to 10%, as business needs require). What do we offer? Insurance Plans (Medical, Life)\nPension/401K/RSP (country specific)\nCompetitive Bonus\nMobility Allowance\nTuition Reimbursement\nCompany Holidays\nVolunteering time\nAnd More.....

Compensation:

The base pay range for this role is $102,700 - $128,400 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. The Company is only considering applicants who are currently authorized to work in the country the position is based. 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