Blend360
Technology
DataQAAnalyst
Neural analysis suggests this role is
optimal for mid candidates.
“Data QA Analyst at Blend360. Skills: Data Quality, Data Pipelines, Azure, Databricks. Design data quality framework. Implement data quality framework”
Industry & Context.
Analytical thinking; Troubleshooting; Root cause analysis
What They're Looking For.
Must Have
1+ years of experience in Data Quality, 1+ years of experience in Data Engineering, 1+ years of experience in Data Analysis, Azure-based data platforms experience, Databricks experience, Data quality frameworks understanding, Data pipeline testing methodologies understanding, ETL/ELT processes validation experience, Layered architectures experience, SQL skills, Large datasets analysis experience, Automated data validation implementation experience, Automated data reconciliation implementation experience, Data pipeline monitoring familiarity, Data pipeline alerting familiarity, Data pipeline troubleshooting familiarity, English: Advanced
Nice to Have
GCP Professional Data Engineer certification, AWS Data Analytics certification, Databricks Certified certification, Dbt Certified certification, PhD preferred
What You'll Do.
Design data quality framework
Implement data quality framework
Define validation rules
Define threshold tolerances
Define alerting standards
Build automated data quality checks
Maintain automated data quality checks
Own reconciliation between source systems
Own reconciliation to Databricks layers
Validate identity resolution outputs
Investigate false positives
Investigate false negatives
Ensure enterprise identifiers assigned correctly
Perform end-to-end pipeline testing
Validate downstream reporting outputs
Define acceptance criteria
Validate Gold layer outputs
Document QA processes
Document test results
Document data quality findings
Monitor pipeline health post-deployment
Investigate data quality incidents
Triage data quality incidents
How You'll Work.
Team & Collaboration
Collaborate with Data Engineering; Collaborate with business teams; Partner with Data Engineers; Collaborate with stakeholders; Support UAT with client business stakeholders
Communication Scope
English: Advanced
Process & Methodology
Agile, Sprint planning
Full Job Description
Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit [www.blend360.com](http://www.blend360.com/) We are seeking a Data QA Analyst to contribute to our next level of growth and expansion. What is this position about? We are looking for a Data QA Analyst with experience in Azure and Databricks to ensure data quality, reliability, and consistency across modern data platforms. This role focuses on validating data pipelines, implementing automated quality checks, and collaborating closely with Data Engineering and business teams to guarantee accurate and production-ready data assets. * Design and implement a data quality framework across Bronze, Silver, and Gold layers — defining validation rules, threshold tolerances, and alerting standards * Build and maintain automated data quality checks within Databricks pipelines — row counts, null checks, referential integrity, schema validation, and business rule assertions * Own reconciliation between source systems and Databricks layers — ensuring source data lands accurately and transformations produce expected outputs * Validate identity resolution outputs in the Silver layer — reviewing match rates, investigating false positives and false negatives, and ensuring enterprise identifiers are being assigned correctly across source populations * Perform end-to-end pipeline testing — validating that data flows correctly from ingestion through to the Gold lay
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