Senior Data Quality Engineer

Job Title: Senior Data Quality Engineer

Location: Chicago, IL (hybrid)

About The Company

We are one of the world's largest and most prestigious full-service law firms, with a global footprint spanning more than 20 offices across the Americas, Europe, and Asia Pacific. With a history stretching back over 160 years, we have built a reputation for delivering sophisticated legal counsel to Fortune 500 companies, financial institutions, governments, and leading organizations across virtually every major industry and practice area from corporate transactions and litigation to regulatory matters, finance, and restructuring. We are committed to a culture of excellence, collaboration, and inclusion, where talented professionals at every level are supported, challenged, and empowered to do their best work.

Job Summary

We are seeking a Senior Data Quality Engineer to join our team. This is a hands-on technical role at the heart of our data platform strategy. In this position, you will design and build robust, scalable testing frameworks for modern data pipelines, ensuring data quality and reliability from ingestion through transformation. You will work across the full Medallion Architecture (Bronze, Silver, Gold), validating data from a variety of sources including APIs, SQL Server, and flat files. Your work will directly power downstream analytics in tools like Power BI and Tableau, making data quality central to business decision-making.

Key Responsibilities

• Design and implement scalable, end-to-end testing frameworks for data pipelines across all ingestion and transformation layers.
• Validate data ingestion from APIs, SQL Server, and flat files, ensuring accuracy, completeness, and consistency.
• Enforce data quality standards across Medallion Architecture layers, from raw Bronze data through curated Gold outputs.
• Build automated checks for schema validation, data integrity, and transformation accuracy.
• Develop reusable validation patterns using Great Expectations or comparable frameworks.
• Leverage AI-assisted development tools and prompt engineering techniques to accelerate test generation, standardization, and automation.
• Create and maintain reusable AI-driven testing assets including workflow templates and prompt libraries to support scalable, consistent testing practices.
• Integrate testing workflows into CI/CD pipelines using Azure DevOps, GitHub Actions, or similar platforms.
• Partner with data engineers, product managers, and DevOps teams to define data quality standards and acceptance criteria.
• Monitor data reliability and observability metrics, continuously improving test coverage and pipeline health.
• Investigate data quality issues, perform root-cause analysis, and drive resolution across teams.
  
  

Requirements

Education & Experience:

• Bachelor's degree in Computer Science, Information Technology, or a related field (or equivalent experience)
• At least 5 years of experience in data quality engineering, data testing, or data platform engineering
• Proven experience building automated testing frameworks for data pipelines
• Hands-on experience with data validation tools such as Great Expectations, DQX, or dbt tests
• Proficiency in Python and strong SQL skills
• Experience working with AI-assisted development tools (such as GitHub Copilot or similar)
• Solid understanding of prompt engineering concepts and reusable AI workflows
• Experience validating data ingestion from APIs, relational databases, and flat files
• Deep understanding of data transformation validation and Medallion Architecture principles
• Experience integrating testing into CI/CD pipelines (Azure DevOps, GitHub Actions, etc.)
• Familiarity with data observability and monitoring best practices
  
  

Skills

• Strong organizational skills and attention to detail
• Excellent communication skills with the ability to articulate technical concepts to non-technical stakeholders
• Strong analytical and problem-solving abilities
• Ability to collaborate effectively across engineering, product, and operations teams
• Comfortable working under pressure with multiple priorities and deadlines
  
  

Preferred Qualifications

• Experience with Azure Databricks and Apache Spark (PySpark)
• Familiarity with Delta Lake and Unity Catalog
• Experience building reusable prompt libraries or AI skill assets in structured formats
• Background in test data management and synthetic data generation
• Knowledge of data contracts and schema evolution strategies
• Familiarity with infrastructure as code tools such as Terraform or Bicep
• Experience working with streaming data pipelines (Kafka, Azure Event Hubs, etc.)