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

We are seeking a Data QA Analyst to contribute to our next level of growth and expansion.

What is this position about?

• Validate data accuracy and ensure production readiness by working closely with the Data Engineering Manager and Analytics Engineering Lead across 18 data domains.
• Design and build a reconciliation framework to systematically compare legacy pipeline outputs against new pipeline outputs, identifying discrepancies and gaps.
• Execute structured acceptance testing for each pipeline prior to promotion to production environments.
• Validate identity resolution accuracy through rigorous analysis of match rates, false positives, and false negatives.
• Document end-to-end data lineage across all 18 domains to support auditability, transparency, and regulatory compliance.
• Build and maintain automated regression test suites to enable continuous quality assurance as pipelines evolve.

• Expert-level SQL skills, with demonstrated ability to write complex queries for data validation, profiling, and reconciliation at scale.
• Experience with data quality frameworks, with preference given to candidates with hands-on Great Expectations expertise.
• Proficiency in Python for scripting automated tests, data profiling tasks, and quality checks.
• Strong background in data profiling techniques, including distribution analysis, completeness checks, and anomaly detection.
• Experience designing and executing test automation strategies in data or analytics engineering contexts.
• Ability to clearly document findings and communicate data quality issues to both technical and non-technical stakeholders.

What about languages?

English: Advanced (required for effective communication with global teams).

How much experience must I have?

3+ years of experience in Data Quality, QA, or Data Analysis roles, with proven ability to build reconciliation frameworks and execute end-to-end acceptance testing across complex data pipelines.

Our Perks and Benefits:

📚 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. For detailed information regarding the benefits applicable to your specific location, please consult with one of our recruiters.

So what are the next steps? Our team is eager to learn about you! Send us your resume or LinkedIn profile below and we'll explore working together!