数据质量工程师
Data Quality Engineer
Blend 是一家领先的 AI 服务提供商，致力于通过数据科学、人工智能、技术和人才的力量，与客户共同创造有意义的影响。公司使命是激发大胆的愿景，通过无缝结合人类专业知识与人工智能，解决重大挑战。公司通过世界级的人才和数据驱动的战略，为客户提供价值并推动创新。我们相信，人与 AI 的力量可以对您的世界产生深远影响，为我们的员工和客户创造更有意义的工作和项目。更多信息，请访问 www.blend360.com
我们正在寻找一名数据质量工程师，以支持我们的下一轮增长和扩展。
这个职位是做什么的？
我们正在寻找一位拥有丰富 Azure 和 Databricks 经验的数据质量工程师，以确保现代数据平台上的数据质量、可靠性和一致性。该职位专注于验证数据管道、实施自动化质量检查，并与数据工程和业务团队紧密合作，确保数据资产准确且可投入生产。
- 在青铜层、银层和金层设计并实现数据质量框架 —— 定义验证规则、阈值容差和警报标准
- 在 Databricks 管道中构建和维护自动化数据质量检查 —— 行数检查、空值检查、参照完整性、模式验证和业务规则断言
- 负责源系统与 Databricks 层之间的对账 —— 确保源数据准确落地，转换生成预期输出
- 验证银层的身份解析结果 —— 审查匹配率，调查误报和漏报，并确保企业标识符在源人群中的正确分配
- 执行端到端管道测试 —— 验证数据从摄入到金层的流程是否正确，以及下游报告输出是否反映准确数据
- 与数据工程师合作，在管道和数据模型交付物上线生产前定义每个冲刺周期的验收标准
- 与客户业务利益相关者一起进行用户验收测试（UAT）—— 帮助他们验证金层输出是否符合他们的报告需求
- 以可交接给客户团队的方式记录所有 QA 流程、测试结果和数据质量发现
- 监控管道
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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 Quality Engineer to contribute to our next level of growth and expansion.
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!