Lead Snowflake Engineer

We are seeking a Lead Snowflake Engineer to design, build, and operate robust data pipeline infrastructure that ingests from diverse source systems, normalizes data into consumption-ready models in Snowflake, and delivers clean, reliable datasets for Power BI reporting. This role is the connective tissue between raw data sources and business intelligence — you will own the full pipeline lifecycle from source connectivity through to report-ready data layers.

• 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.

• 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.
• 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 Direct Query 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.

• PostgreSQL & relational databases — Query optimization, indexing, and schema design patterns.
• Cloud data infrastructure — Azure Data Factory, Event Hubs, AWS S3, or equivalent as source or orchestration layers.
• dbt — Transformation layer experience with models, tests, documentation, and lineage tracking strongly preferred.
• Data observability & testing — Experience with dbt tests, Great Expectations, Monte Carlo, or equivalent quality frameworks.