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
Job DescriptionWe’re looking for a QA Data Engineer to support the validation, reconciliation, and production readiness of data pipelines for a high-impact enterprise initiative. This hybrid role is ideal for someone who combines strong analytical capabilities with hands-on experience in data quality, testing, and validation frameworks.
You will play a key role in ensuring data accuracy and reliability across multiple domains, working closely with Data Engineering and Analytics teams to validate and promote high-quality data pipelines into production.
What will you be doing?
- Validate data accuracy across pipelines, ensuring consistency between legacy and new systems.
- Build and maintain reconciliation frameworks to compare outputs between legacy and modernized data pipelines.
- Execute acceptance testing for data pipelines prior to production deployment.
- Validate identity resolution accuracy, including match rates and false positive/negative analysis.
- Develop and maintain automated regression test suites to ensure ongoing data quality.
- Perform data profiling and analysis to detect anomalies and inconsistencies.
- Document data lineage across multiple domains (up to 18 domains).
- Collaborate with Data Engineering and Analytics teams to ensure production readiness of data assets.
What are we looking for?
- 3+ years of experience in Data Quality, QA, or Data Analysis roles.
- Expert-level proficiency in SQL for data validation and analysis.
- Hands-on experience with data quality frameworks (Great Expectations preferred).
- Strong experience with Python for data validation, automation, or analysis.
- Experience with data profiling, reconciliation, and validation processes.
- Experience building or maintaining test automation frameworks for data pipelines.
- Strong attention to detail with a focus on data accuracy and reliability.
- Ability to work in cross-functional environments and collaborate effectively with engineering and analytics teams.
Nice to have
- Experience working with large-scale or distributed data platforms.
- Familiarity with modern data architectures and pipeline validation processes.
- Experience supporting production deployments and release validation processes.
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.
Skills Required
- 3+ years of experience in Data Quality, QA, or Data Analysis roles
- Expert-level proficiency in SQL for data validation and analysis
- Hands-on experience with data quality frameworks
- Great Expectations (preferred implementation of data quality framework)
- Strong experience with Python for data validation, automation, or analysis
- Experience with data profiling, reconciliation, and validation processes
- Experience building or maintaining test automation frameworks for data pipelines
- Strong attention to detail with a focus on data accuracy and reliability
- Ability to work in cross-functional environments and collaborate with engineering and analytics teams
- Experience with large-scale or distributed data platforms
- Familiarity with modern data architectures and pipeline validation processes
- Experience supporting production deployments and release validation processes
Blend360 Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Blend360 and has not been reviewed or approved by Blend360.
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Fair & Transparent Compensation — Pay is considered fair-to-good by many, and public salary postings for common data roles indicate competitive packages in numerous markets. Feedback suggests overall company sentiment aligns with acceptable compensation relative to peers in consulting and analytics.
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Flexible Benefits — Flexible and remote/hybrid work arrangements are consistently highlighted in official materials and role descriptions. Feedback suggests flexibility is a meaningful part of the total rewards experience.
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Retirement Support — A 401(k) with company match is part of the core package. Feedback suggests retirement offerings are standard and contribute to a complete benefits set.
Blend360 Insights
What We Do
Our Vision is to build a company of world-class people that helps our clients optimize business performance through data, technology and analytics. Blend360 has two divisions: Data Science Solutions: We work at the intersection of data, technology and analytics. Talent Solutions: We live and breathe the digital and talent marketplace.
