What are the responsibilities and job description for the Snowflake Data Engineer position at Reuben Cooley?
About the Role:
Build and scale cloud data products on Snowflake supporting institutional trading, risk, and regulatory reporting. This is a hands-on build role — you will own pipelines end-to-end from ingestion through curated, governed consumption layers used by front-office, risk, and control functions.
Responsibilities
- Design, develop, and optimize Snowflake data models (staging → integration → semantic/consumption layers) for institutional trade, position, reference, and market data.
- Build ingestion pipelines using Snowpipe / Snowpipe Streaming, Streams & Tasks, Dynamic Tables, and external stages against S3.
- Develop transformation logic in SQL and dbt with version control, CI/CD, and automated testing.
- Tune performance and cost: warehouse right-sizing, clustering keys, micro-partition pruning, query profiling, result caching, resource monitors, and credit-consumption reporting.
- Implement data security and entitlements: RBAC hierarchy, dynamic data masking, row access policies, secure views, and secure data sharing across LOBs.
- Migrate legacy Oracle / Teradata / Sybase / Hadoop workloads to Snowflake, including reconciliation and parallel-run validation.
- Partner with data governance on lineage, cataloging, data quality rules, and audit/regulatory evidence.
Qualifications:
- 7 years in data engineering; 4 years hands-on Snowflake in a production environment.
Required Skills:
- Expert SQL — window functions, CTEs, complex joins, query optimization, execution plan analysis.
- dbt (Core or Cloud) — models, macros, snapshots, tests, exposures.
- Orchestration: Airflow, Control-M, or Autosys.
- AWS: S3, IAM, Glue, Lambda, Secrets Manager.
- CI/CD and IaC: Git, Jenkins/GitHub Actions, Terraform or Schema change for Snowflake object deployment.
- Dimensional and Data Vault modeling; slowly changing dimensions; late-arriving data handling.