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.

Key Responsibilities

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.

Required Qualifications

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).

Preferred Qualifications

PostgreSQL: Working knowledge of PostgreSQL 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).

Certifications

None specified.

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