
Interesting Job Opportunity: Atlas Systems - Quality Assurance & Data Quality Engineer
Atlas Systems • Bengaluru, Karnataka, India
Role & seniority
- Senior BI Quality Assurance leader (enterprise BI QA & Data Quality)
Stack / tools
Microsoft Fabric ecosystem: OneLake, Lakehouse, Dataflows, Notebooks, Semantic models
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Power BI (reports, datasets, service), DAX, SQL Server / Azure SQL / Fabric SQL
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Automation: Playwright for BI UI validation; CI/CD integrations; Python scripting
Top 3 responsibilities
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Define and own end-to-end BI QA & Data Quality framework across ingestion, transformation, semantic models, and reporting
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Establish independent BI QA governance (QA vs. developer testing) and manage release gating, certification, and production readiness
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Design/implement data quality and semantic validation, automation roadmap, and shift-left quality with platform-team collaboration; mentor senior QA staff
Must-have skills
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10+ years in BI/analytics or data quality; 4+ years in leadership/enterprise ownership
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Deep Microsoft analytics expertise: Power BI, semantic models, DAX; SQL (Server/Azure/Fabric)
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Hands-on automation for BI (Playwright or similar UI automation) plus SQL and Python
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Ability to influence developers and stakeholders; drive process changes and test strategy
Nice-to-haves
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Experience with Microsoft Fabric components (OneLake, Lakehouse, Dataflows, Notebooks, Synapse/Fabric SQL)
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Background in regulated sectors (e.g., financial services)
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Knowledge of metric governance, data lineage, BI certification frameworks
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Experience with visual regression testing or BI monitoring
Full Description
Description
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 Fabriccentric 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.
(ref: hirist.tech)