QA Analyst, Databricks

QA Analyst ensuring data quality, reliability, and consistency for Blend's data platforms, collaborating with Data Engineering and business teams.

Posted 4/22/2026full-timeRemote • 🇨🇱 ChileMid-LevelSeniorWebsite

Tools & technologies

AzureETLSQL

Key responsibilities & impact

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 layer and that downstream reporting outputs reflect accurate data.
Partner with Data Engineers to define acceptance criteria for each sprint’s pipeline and data model deliverables before they are promoted to production.
Support UAT with client business stakeholders — helping them validate that Gold layer outputs meet their reporting requirements.
Document all QA processes, test results, and data quality findings in a format that can be handed off to the client team at engagement close.
Monitor pipeline health post-deployment — investigating and triaging data quality incidents and working with engineers to resolve root causes quickly.

What you’ll need

Experience working with Azure-based data platforms, including Databricks.
Strong understanding of data quality frameworks and testing methodologies for data pipelines.
Experience validating ETL/ELT processes and working with layered architectures (Bronze, Silver, Gold).
Strong SQL skills and experience analyzing large datasets.
Experience implementing automated data validation and reconciliation processes.
Familiarity with data pipeline monitoring, alerting, and troubleshooting.
Ability to collaborate with Data Engineers and business stakeholders.
Strong analytical thinking and attention to detail.
Experience documenting QA processes and results in a structured manner.
English: Advanced (required for effective communication with global teams).

Comp & perks

📚Learning Opportunities: Certifications in AWS (we are AWS Partners), Databricks, and Snowflake.
Access to AI learning paths to stay up to date with the latest technologies.
Study plans, courses, and additional certifications tailored to your role.
Access to Udemy Business, offering thousands of courses to boost your technical and soft skills.
English lessons to support your professional communication.
👨🏽‍💻Travel opportunities to attend industry conferences and meet clients.
👩‍🏫 Mentoring and Development: Career development plans and mentorship programs to help shape your path.
🎁 Celebrations & Support: Special day rewards to celebrate birthdays, work anniversaries, and other personal milestones.
Company-provided equipment.
⚖️ Flexible working options to help you strike the right balance.
Other benefits may vary according to your location in LATAM.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

data quality frameworkautomated data quality checksDatabricksETLELTSQLdata validationdata reconciliationpipeline testingdata analysis

Soft Skills

analytical thinkingattention to detailcollaborationcommunication