Sr Business Intelligence Engineer

NetDocuments•Lehi, UT

5h•Hybrid

About The Position

NetDocuments is seeking a highly skilled Senior BI Engineer to take ownership of our Snowflake data platform and help evolve it into a scalable, well-governed foundation for analytics and AI. This is a hands-on engineering role responsible for shaping data architecture, enforcing standards, and building a clean, reliable data layer that supports both business intelligence and AI-driven use cases. You will report directly to the Director of BI and have significant autonomy in how the platform evolves.

Requirements

5+ years of hands-on data engineering experience, with a meaningful portion spent owning architecture and driving development — not just executing assigned tasks.
Strong experience with Snowflake as a primary warehouse.
Advanced SQL skills and experience building production-grade ELT pipelines.
Strong hands-on experience with dbt for modeling, testing, and transformation — including macros, tests, snapshots, and managing a production dbt project at scale.
Experience integrating and normalizing data from many disparate source systems — SaaS APIs, application databases, CRM/ERP, finance systems, event data — into consistent, analytics-ready models.
Deep, hands-on experience with star schemas and dimensional modeling, SCD Type 2 implementations, and layered/medallion architectures.
Ability to make tradeoffs and defend decisions (performance vs flexibility, normalization vs usability, etc.).
Track record of improving data quality, consistency, and trust.
Experience implementing Git-based workflows and structured SDLC practices for data.
Strong instincts around testing, governance, and maintainability.
Comfortable setting and enforcing standards across a team.
Demonstrated ability to use AI tools to meaningfully increase output and velocity.
Strong judgment to validate and refine AI-generated work.
Interest in applying AI/agents to improve data engineering workflows.

Nice To Haves

Familiarity with Power BI
Familiarity with Salesforce as a data source (objects, schema, Salesforce-to-warehouse pipelines)
Experience working with or leading offshore/support resources

Responsibilities

Own and Elevate the Data Platform: Own the design and evolution of our Snowflake data platform end-to-end. Refactor and enhance pipelines, dbt models, and transformations to meet high standards for scalability, performance, and maintainability. Make and drive architectural decisions—not just implement tasks.
Architect for Scale and Clarity: Implement and enforce medallion architecture (bronze/silver/gold) with clear separation of concerns. Design and build dimensional models (star schemas) that are intuitive, performant, and durable. Implement SCD Type 2 and other core warehousing patterns correctly and consistently. Establish and enforce standards for schemas, naming, and data contracts. Integrate and normalize data from a wide range of source systems (SaaS apps, operational databases, event streams, finance/CRM tools) into a coherent, well-modeled warehouse layer.
Build an AI-Ready Data Layer: Design data models optimized for semantic clarity and AI/LLM consumption. Implement semantic views in Snowflake to enable tools like Claude via MCP. Ensure data is traceable, explainable, and trustworthy.
Establish a Real Data Engineering SDLC: Implement a Git-driven development lifecycle (branching, PR reviews, testing, deployments). Introduce and enforce data quality testing and validation, schema enforcement and regression safeguards, and clear promotion paths from dev → prod. Raise the bar on engineering discipline across the data function.
Leverage AI as a Force Multiplier: Use AI tools to accelerate development while maintaining high standards. Guide and validate AI-generated code—never blindly trust it. Explore agent-based approaches for data quality auditing, pipeline validation, and anomaly detection and monitoring.
Collaborate and Lead Technically: Partner with BI analysts to deliver reliable, well-modeled datasets (Power BI downstream). Translate ambiguous business requirements into clean, scalable data models. Provide technical direction to a supporting QA/offshore resource (planned).
Documentation & Transparency: Build a culture of clear documentation, lineage, and data discoverability. Ensure systems are understandable and maintainable by others.
Other duties as assigned.