Power BI Engineer

Senior Business Intelligence Engineer (Power BI)
Experience level: 5+ years
Function:
Data & Analytics / Business Intelligence
Engagement type:
Client-facing delivery (consulting / embedded analytics)

About the Role
We're looking for a Senior BI Engineer who can own the full lifecycle of analytics delivery, from data modeling in the warehouse through to polished, secure, production-grade Power BI reporting embedded in client-facing applications. This is a hybrid technical and consultative role. You'll build the reports, engineer the data behind them, and communicate directly with client stakeholders about priorities, trade-offs, and progress.
You'll work on a mature analytics program spanning executive dashboards, member/partner performance reporting, geospatial analytics, embedded portal analytics, and emerging AI-driven conversational analytics. The right person is equally comfortable debugging a row-level security conflict in DAX, tuning a Snowflake view, and explaining a data limitation to a non-technical business owner in plain language.

What You'll Do

• Design, build, and maintain Power BI reports and dashboards, from data model through DAX measures to final layout and interactivity.

• Implement and troubleshoot row-level security (RLS), including dynamic/organization-scoped security and its interaction with export, embedding, and cross-group comparison logic.

• Develop robust semantic models using Power Query / M and DAX, with attention to performance, maintainability, and correctness under security filters.

• Build and support embedded analytics delivered through a partner/portal web application (Power BI Embedded), including effective-identity token handling.

• Engineer and optimize the data layer in Snowflake, writing SQL, building and debugging views, and diagnosing hidden dependencies such as masking policies, row-access policies, and view lineage.

• Integrate external and third-party data sources (e.g., public datasets, government/nonprofit APIs, demographic and geospatial data) into the warehouse and reporting layer, including handling API versioning and schema changes over time.

• Build geospatial visuals, including map layers and reference geometries, and work through platform constraints such as feature caps, boundary data quality, and geocoding issues.

• Contribute to AI-assisted analytics initiatives (e.g., conversational/natural-language analytics over governed datasets).

• Own data validation and UAT for new pipelines and reports, verifying correctness across raw, curated, and reporting layers before and after release.

• Support data governance practices, including data cataloging and documentation, and identifying and handling sensitive/PII data appropriately.

• Translate business requirements into technical specifications, and communicate status, blockers, and recommendations directly to client stakeholders in a clear, professional, conversational tone.

• Produce clean documentation and work breakdowns; scope and estimate level of effort for new requests.

Must-Have Qualifications

• 5+ years of hands-on BI/analytics engineering experience, with a strong concentration in Power BI.

• Expert-level DAX and Power Query / M, able to write, optimize, and debug complex measures and transformations.

• Strong data modeling fundamentals (star schema, relationships, cardinality, and their impact on report behavior).

• Deep, practical experience with row-level security in Power BI and its edge cases around export and embedding.

• Advanced SQL (required) and solid experience with a cloud data warehouse. Snowflake strongly preferred (or Redshift/Big Query/Synapse with willingness to ramp on Snowflake).

• Python (required) for data work, including API integration, data prep/transformation, notebook-based workflows, and automation/scripting.

• Solid grasp of data warehousing and ELT/ETL concepts and best practices.

• Discipline around data validation, QA, and testing, plus awareness of data governance and sensitive/PII handling.

• Experience delivering in a client-facing or stakeholder-facing capacity: gathering requirements, managing expectations, and explaining technical concepts to non-technical audiences.

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