What is this position about?
We are looking for a Data Quality Engineer with strong 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 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
- 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.
What about languages?
- English: Advanced (required for effective communication with global teams).
How much experience must I have?
- 3+ years of experience in Data Engineering or Data Quality roles.
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!