Data Quality Engineer

Blend360

21h•Hybrid

About The Position

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. We are seeking a Data Quality Engineer to contribute to our next level of growth and expansion.

Requirements

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)
3+ years of experience in Data Quality, Data Engineering, or Data Analysis roles

Responsibilities

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

Benefits

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
Career development plans and mentorship programs to help shape your path
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