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. #J-18808-Ljbffr