QA Lead(BI Quality Assurance & Data Quality)

About Us:

Atlas Systems Inc. is a Software Solutions company headquartered in East Brunswick, NJ. Incorporated in 2003, Atlas provides comprehensive range of solutions in the area of GRC, Technology, Procurement, Healthcare Provider and Oracle to customers across the globe. Combining our unparalleled experience of over a decade in the software industry and global reach, we have grown with extensive capabilities across industry verticals.

For more information, please visit our website
https://www.atlassystems.com/

https://atlas.bamboohr.com/careers/509

Job Title

BI Quality Assurance & Data Quality (Microsoft Fabric & Power BI)

Role Summary

We are seeking a senior BI Quality Assurance leader to establish and own an enterprise BI QA and Data Quality function within a Microsoft Fabric–centric analytics environment. This role is responsible for creating independent, auditable BI quality practices in an ecosystem where BI developers historically self-test reports.

The ideal candidate will define strategy, build automation (including Playwright for BI UI validation), and mentor senior BI QA professionals while partnering closely with BI, Data Engineering, and business stakeholders.

Key Responsibilities

BI QA Strategy & Ownership

• Define and lead the end-to-end BI QA and Data Quality framework across ingestion, transformation, semantic models, and reporting layers.
• Establish independent BI QA governance, separating developer testing from QA assurance and production sign-off.
• Own release gating, certification, and production readiness for mission-critical BI assets.

Data Quality & Semantic Validation

• Design and enforce data quality standards (accuracy, completeness, consistency, timeliness, reconciliation).
• Own QA of Power BI semantic models, including DAX measures, KPIs, hierarchies, and time-intelligence logic.
• Ensure metric consistency across dashboards, business units, and reporting periods.

Automation & Tooling

• Define and implement BI QA automation strategy combining:
  • SQL / Fabric-native data validations (OneLake, Lakehouse, Fabric SQL).
  • Semantic-layer and metric-level validations.
  • Playwright-based UI automation for Power BI reports (filters, slicers, drill-through, exports, rendering, and visual regression).
• Integrate BI QA automation into CI/CD pipelines and scheduled monitoring.

Microsoft Fabric Enablement

• Leverage Microsoft Fabric capabilities (OneLake, Dataflows, Notebooks, Lakehouse, Semantic models) to enable shift-left quality and automation.
• Partner with platform teams to utilize lineage, metadata, and cataloging for test traceability and root-cause analysis.

Leadership & Mentorship

• Lead and mentor senior BI QA engineers; establish skill standards across SQL, DAX, Fabric, and automation tooling.
• Coach BI developers on building testable analytics and adopting quality-first practices without slowing delivery.

Stakeholder & Risk Management

• Act as the final quality authority for executive and business-critical dashboards.
• Partner with business, engineering, and compliance teams to manage BI risk, auditability, and incident response.

Required Qualifications

• 10+ years in BI, analytics, or data quality roles, with 4+ years in leadership or enterprise ownership positions.
• Proven experience building or leading BI QA / Data Quality functions beyond traditional ETL testing.
• Strong expertise in the Microsoft analytics ecosystem, including:
  • Power BI (reports, datasets, service)
  • Semantic models and DAX
  • SQL Server / Azure SQL / Fabric SQL
• Hands-on experience designing or implementing automation for BI, including Playwright or similar UI automation frameworks.
• Advanced SQL skills and working knowledge of Python or equivalent scripting languages.
• Demonstrated ability to influence BI developers and business stakeholders and drive process change.

Preferred Qualifications

• Experience with Microsoft Fabric (OneLake, Lakehouse, Dataflows, Notebooks, Synapse/Fabric SQL).
• Background in financial services, asset management, or regulated data environments.
• Familiarity with metric governance, data lineage, and BI certification frameworks.
• Experience with visual regression testing or BI monitoring/anomaly detection.

What Success Looks Like

• Mission-critical BI metrics are trusted, certified, and independently validated.
• BI QA is embedded into delivery pipelines without becoming a bottleneck.
• Automated tests cover high-risk dashboards and semantic models.
• Data incidents impacting executive reporting are materially reduced.
• BI QA engineers are technically strong, well-mentored, and aligned to a clear quality vision.