Job Requirements

Job Requirements

Snowflake Engineer

5 years overall; 3 years of hands-on Snowflake development

Snowflake migration and modernization

Role purpose

Develop, test, optimize, and support Snowflake and dbt pipelines for layered transformation, coexistence, reconciliation, and downstream consumption.

Program context

Enterprise modernization of legacy Oracle CTM/ADW analytics to a Kraken-fed Snowflake platform. In scope are Databricks Delta Sharing ingestion, append-only Bronze, dbt-based Silver and Gold models, Snowflake Dynamic Tables for dual-source coexistence, automated reconciliation, downstream data products/file exports, and phased production rollout.

Key responsibilities

· Develop Snowflake SQL and dbt models across Bronze, Silver, and Gold layers from approved mappings and data contracts.

· Build append-only ingestion and materialization logic for Databricks-shared data with audit columns, load controls, monitoring, and replay capability.

· Refactor legacy SQL views and business rules into modular dbt models, reusable macros, documentation, and version-controlled code.

· Implement dimensional models, SCD Type 1 entities, incremental/micro-batch processing, and hourly/daily/seasonal interval rollups.

· Create Dynamic Tables, Tasks, control/mapping tables, and backward-compatible views for legacy/Kraken coexistence.

· Write dbt and SQL tests for schema, uniqueness, referential integrity, business rules, row counts, aggregates, and reconciliation.

· Tune SQL using query profiles, pruning, clustering, warehouse selection, and efficient transformation patterns.





· Participate in peer reviews, Git/CI-CD, SIT/UAT, defect resolution, deployment, documentation, production support, and downstream validation.

Mandatory skills and experience

· Strong Snowflake SQL, loading patterns, stages, Tasks, Streams, Dynamic Tables, views, warehouses, and query optimization.

· Hands-on dbt models, tests, macros, documentation, incremental processing, Git, and CI/CD.

· Advanced SQL including joins, window functions, CTEs, aggregation, profiling, and reconciliation.

· Dimensional modeling, ELT/ETL, source-to-target mapping, data-quality validation, and production troubleshooting.

· Python or PySpark and cloud object-storage experience, preferably AWS.

Preferred skills

· SnowPro Core or dbt certification.

· Databricks Delta Sharing experience.

· Time-series, meter, billing, telemetry, Tableau, SFTP, or file-output experience.

Expected outcomes

· Production-ready, tested, documented, and supportable Snowflake/dbt solutions.

· Preserved schema contracts and downstream continuity during phased coexistence.

· Demonstrated reconciliation between legacy Oracle-origin and Kraken-origin data.

· Performance, freshness, reliability, security, and Snowflake consumption within agreed targets.

· Explicit runbooks, technical documentation, and knowledge transfer.

Working model

Work closely with Snowflake, Databricks/Kraken, legacy Oracle, platform/security, QA, business data owners, and downstream consumer teams. Participate in Agile ceremonies, design reviews, delivery governance, risk management, cutover, and production-readiness activities.

📌 Snowflake Data Engineer (Pune)
🏢 Xoriant
📍 Pune