Snowflake Data Engineer

Role: Snowflake Data Engineer

Location :- NY & NJ (Hybrid)

Position Type: Fulltime- No Contract

Job Description:- 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.

Key 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.

·       Support production: incident triage, root-cause analysis, SLA adherence, and on-call rotation for critical batch cycles.

·       Work in Agile squads with BAs, QA, and platform engineering; participate in design reviews and code reviews.

Required Qualifications

·       7+ years in data engineering; 4+ years hands-on Snowflake in a production environment.

·       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.

·       Demonstrated Snowflake cost-governance ownership (not just development).