Lead Snowflake Engineer

Anblicks; Richardson (TX); On-site; USD 140,000 - 190,000; Full time

Job summary

Anblicks is seeking a Lead Snowflake Engineer to design, build, and operate robust data pipeline infrastructure that ingests from diverse sources and delivers clean, consumption-ready datasets for Power BI reporting.

You will own the full pipeline lifecycle from source connectivity through to Snowflake data models, focusing on performance, data quality, and observability in a cloud-based environment.

Qualifications

• Expert Snowflake engineering with schemas, SQL (PIVOT, GROUPING SETS, ROLLUP/CUBE).
• Proven ability to build end-to-end ELT pipelines from multiple sources.
• Strong Power BI integration experience and data modeling.
• Experience with Python for orchestration and automation.
• Familiarity with Snowflake Cortex and AI agent patterns is a plus.

Responsibilities

• Design and implement end-to-end ELT pipelines from APIs, databases, and SaaS to Snowflake.
• Architect schemas and manage Snowflake environments and cost governance.
• Transform raw data into consumption-ready dimensional models for BI.
• Build and maintain reliable source connectors and data quality checks.
• Optimize Snowflake layers for Power BI performance and productivity.
• Leverage Cortex and AI agents to automate data workflows.
• Ensure data quality, observability, and monitoring across pipelines.

Skills

Snowflake Expert

SQL Advanced

ELT pipelines

Power BI alignment

Python

AI Agents in Snowflake

Tools

dbt

Azure Data Factory

AWS S3

Job description

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.

Core 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.
• 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.

Additional Skills

• 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.