Data Quality Engineer

About the Role

As a Data Quality Engineer embedded in Anza MI’s Data Science & Engineering practice, you will be the primary guardian of data integrity for our mortgage-related datasets. Our team leverages a cloud-native technology stack and uses Databricks for data pipelines to analyze data and automate operational processes. Your primary responsibility will be to design, automate, and monitor data validation frameworks that support our predictive models and analytics workflows. You will adhere to engineering best practices established by the wider Technology organization. A Data Quality Engineer will work autonomously and in partnership with senior team members and business partners to ensure trust in our data assets.

Responsibilities

• Data Validation & Monitoring: Build, maintain, and scale automated data quality checks natively within our Databricks environment.
• Pipeline Integration: Implement robust testing and validation gates within our ETL/ELT pipelines to support analytics and modeling workflows.
• Anomaly Detection: Investigate, diagnose, and resolve data discrepancies or pipeline failures impacting mortgage credit risk reporting.
• Team Collaboration: Facilitate the Data Science & Engineering team’s goals by enforcing data governance standards and following engineering practices of the Technology team.
• Continuous Learning: Improve engineering skills and deepen your understanding of the mortgage and housing finance markets.

Qualifications

• Education:
  • Minimum: Bachelor’s degree in Computer Science, Engineering, Data Science, or related fields.
  • Preferred: Master’s degree in Computer Science, Engineering, Data Science, or related fields.
• Experience:
  • Minimum: 3 to 4 years.
  • Preferred: 5+ years.
• Skills:
  • Minimum: Proficiency in Python and SQL, with a strong emphasis on writing optimized queries. Solid understanding of data quality dimensions, cloud platforms (e.g., AWS, Azure), and core data modeling concepts. Hands-on experience implementing data validation using standard test frameworks and Spark testing techniques.
  • Preferred: Deep proficiency in Apache Spark and the Databricks ecosystem. Experience with additional data validation frameworks (e.g., dbt tests, Great Expectations, Soda, Deequ, Monte Carlo), software engineering, and CI/CD practices. Experience with data analysis in a business setting.
• Certification(s):
  • Preferred: Databricks Certified Data Engineer Professional or Associate.

Benefits

• Competitive Compensation – Including salary and performance bonuses.
• Comprehensive Benefits – Health, dental, vision, and mental wellness support.
• Retirement Savings – 401(k) with company matching.
• Career advancement opportunities with business growth.
• Inclusive Culture – A diverse, collaborative, and supportive workplace where every voice is valued.
• Perks & Extras – Generous PTO, team events, wellness programs, and more.