Snowflake Data Engineer

Job Summary

Responsible for designing and operating data pipelines that connect heterogeneous source systems, normalizing data into consumption-ready models in Snowflake, and building AI-powered agents that surface intelligence across the platform.

• Snowflake engineering:
  Design schemas, write performant SQL, manage roles, warehouse sizing, and implement change management practices.
• ETL/ELT development:
  Build and maintain pipelines that ingest from diverse sources (APIs, databases, event streams) and normalize data for BI and downstream consumers.
• AI agent development:
  Leverage Snowflake Cortex and AI agent frameworks to build intelligent data products and automate analytical workflows.
• Backend API connectivity:
  Develop backend integrations with internal and third‑party systems via REST APIs and backend services.

• 8-10 years of experience.
• Snowflake:
  Hands‑on experience building and optimizing data models, writing advanced SQL (PIVOT, GROUPING SETS, ROLLUP, CUBE), and managing Snowflake environments in production.
• Multisource integration:
  Proven ability to connect and ingest data from heterogeneous sources, including relational databases, REST APIs, SaaS platforms, and event streams.
• ETL/ELT design:
  Experience designing normalized schemas and transformation pipelines that produce clean consumption‑ready data models (star/snowflake schema, dimensional modeling).
• Python:
  Strong proficiency in Python for data engineering tasks, pipeline orchestration, data transformation, API clients, and scripting automation.
• AI agents within Snowflake:
  Familiarity with Snowflake Cortex LLM functions and agent‑based patterns for building intelligent data‑driven workflows inside the Snowflake ecosystem.
• Backend integration patterns:
  Practical experience building backend services and integrations using Python, REST APIs, and related tooling (authentication, pagination, error handling, retry logic).

• Postgre
  
  SQL:
  Working knowledge of Postgre
  
  SQL or equivalent RDBMS, including query optimization, indexing, and schema design patterns.
• Go (Golang):
  Experience building backend services or microservices in Go is a strong differentiator.
• Cloud data infrastructure:
  Familiarity with Azure or AWS data services (e.g., Azure Data Factory, Event Hubs, S3) as source or orchestration layers.
• Data observability/testing:
  Experience with data quality frameworks (e.g., dbt tests) or observability tooling (e.g., Great Expectations, Monte Carlo).

None specified.