Snowflake Developer

Responsibilities Pipeline architecture & delivery - Design and implement end-to-end ELT pipelines that pull from all required source systems (APIs, relational databases, SaaS platforms, event streams) and deliver normalized, Power BI-ready data models in Snowflake. Snowflake engineering - Architect schemas (star/snowflake/dimensional), write performant SQL, manage roles and warehouse sizing, and implement change management and data versioning practices. Data normalization - Transform raw, heterogeneous source data into clean, consistent, consumption-ready layers purpose-built for Power BI semantic models and direct query patterns. Source connectivity - Build and maintain reliable connectors to diverse upstream systems using REST APIs, database drivers, and cloud data services; handle authentication, pagination, retry logic, and error recovery. Power BI optimization - Structure Snowflake data layers (fact/dimension tables, aggregations, pre-computed metrics) to maximize Power BI report performance and developer productivity. AI-enhanced workflows - Leverage Snowflake Cortex and AI agent frameworks to build intelligent data products and automate analytical and data quality workflows. Data quality & observability - Implement testing, monitoring, and alerting frameworks to ensure pipeline reliability and downstream data trustworthiness. Required Skills Snowflake (Expert): Snowflake Hands‑on production experience designing schemas, writing advanced SQL (PIVOT, GROUPING SETS, ROLLUP/CUBE), managing Snowflake environments, warehouse sizing, and cost governance. Pipeline & ELT Design: Proven ability to build normalized transformation pipelines that produce clean, consumption‑ready dimensional models from heterogeneous upstream sources. Multi-Source Integration: Track record connecting and ingesting from relational databases, REST APIs, SaaS platforms, and event streams. Strong grasp of source‑specific extraction patterns. Power BI Alignment: Ability to design Snowflake data layers optimized for Power BI - fact/dimension structures, aggregation tables, calculated columns vs. measures trade‑offs, and DirectQuery performance. Python: Strong proficiency for data engineering tasks: pipeline orchestration, data transformation, API clients, scripting automation, and custom connector development. AI Agents in Snowflake : Familiarity with Snowflake Cortex, LLM functions, and agent-based patterns for intelligent, data-driven automation inside the Snowflake ecosystem. #J-18808-Ljbffr