Senior Data Quality Engineer - Zonda - Toronto, ON

Job Description

Senior Data Quality Engineer: Senior Data Quality Engineer - Zonda - Toronto, ON

We are looking for a Senior Data Quality Engineer to build and operate the testing and validation capabilities that ensure data delivered through our enterprise data platform is accurate, complete, consistent, and fit for business use.

Our environment includes Snowflake, AWS Aurora/RDS, Amazon MWAA/Apache Airflow, dbt, AWS Glue, Amazon S3, DataHub, Grafana, and CloudWatch.

This is a hands-on senior engineering role focused on end-to-end data pipeline testing, automated validation, regression testing, test-data management, and business-rule verification.

A major part of the role is partnering with Product, Research, Advisory, Economics, Data Engineering, and other business subject-matter experts to translate business definitions, methodologies, and expected outcomes into measurable and repeatable tests.

Data Engineers remain responsible for testing the pipelines and transformations they build. The Senior Data Quality Engineer provides reusable testing frameworks, curated test data, broader regression coverage, and independent validation for critical or higher-risk changes.

Responsibilities:

Data Pipeline & Regression Testing

• Design and execute testing strategies for ETL/ELT pipelines, transformations, integrations, and published data products.
• Validate data end to end, from source ingestion through transformation and final output.
• Perform source-to-target reconciliation to validate completeness, accuracy, mappings, calculations, aggregations, joins, filters, and business rules.
• Build regression coverage to detect unintended changes caused by pipeline, schema, transformation, or business-rule updates.
• Test large datasets using appropriate sampling, aggregate reconciliation, full-population comparisons, and targeted edge cases.
• Validate nulls, duplicates, referential integrity, incremental processing, historical behavior, distributions, and other material data conditions.

Business-Driven Test Design

• Work with business and domain experts to understand how critical data is expected to behave.
• Translate agreed business definitions, methodologies, calculations, and acceptance criteria into executable test scenarios.
• Identify business-critical attributes, expected outcomes, edge cases, and quality thresholds.
• Validate whether outputs conform to agreed business expectations, not simply whether pipelines execute successfully.
• Maintain traceability between requirements, test cases, defects, and release-validation evidence.

Test Data Management

• Create and maintain curated test datasets and expected results representing common scenarios, edge cases, historical conditions, and known failure modes.
• Work with business SMEs and Data Engineers to identify representative records and expected outcomes.
• Use synthetic, masked, controlled, or approved production-derived data as appropriate to maintain safe test-data practices.
• Maintain reusable baseline datasets as source data, schemas, methodologies, and business rules evolve.

Automation & Quality Frameworks

• Develop reusable automated validation frameworks using SQL, Python, dbt, and other appropriate technologies.
• Automate checks for freshness, volume, completeness, uniqueness, validity, referential integrity, business rules, reconciliation, distribution, and drift.
• Build standardized testing patterns reusable across data products and pipelines.
• Partner with Data Platform Engineering to integrate quality checks into CI/CD, dbt, Airflow/MWAA, and release workflows.
• Enable critical validation failures to prevent downstream execution or publication where agreed quality thresholds are not met.

Release Validation & Monitoring

• Maintain risk-based regression suites for critical data products and pipelines.
• Independently validate material or higher-risk data releases before production publication.
• Compare candidate outputs against approved baselines or production results to identify unexpected differences.
• Provide objective quality evidence, identified risks, and release-readiness recommendations; business and product owners retain responsibility for business acceptance and publication decisions.
• Perform post-release validation for critical changes.
• Establish ongoing monitoring for key data-quality indicators and partner with Data Platform Engineering to surface actionable alerts through Grafana, CloudWatch, DataHub, or related capabilities.
• Investigate quality failures with Data Engineers and convert recurring defects into automated regression tests or preventative controls.

Quality Standards & Enablement

• Establish reusable data-testing standards, methodologies, templates, and best practices.
• Define expectations for test planning, evidence, defect documentation, and risk-based validation.
• Help Data Engineers apply appropriate project-level testing while providing independent validation where additional assurance is warranted.
• Mentor engineering teams on reconciliation, test-data design, regression testing, and data-quality engineering practices.
• Continuously improve test coverage and automation based on business impact and risk.

Basic Qualifications:

• 6+ years of experience in Data Quality Engineering, Data Testing, Quality Engineering, Data Engineering, or a related discipline, including significant experience testing production data systems.
• Strong hands-on experience with ETL/ELT and end-to-end data pipeline testing.
• Strong experience with source-to-target reconciliation, regression testing, and large-dataset validation.
• Advanced SQL skills for data analysis, reconciliation, comparison, and validation.
• Strong scripting or test-automation skills, preferably Python.
• Experience designing automated data-testing or data-quality frameworks.
• Experience creating test cases, expected results, and curated test datasets for complex data scenarios.
• Experience validating data in modern cloud data platforms; Snowflake experience is preferred.
• Experience testing pipelines built with technologies such as dbt, Apache Airflow, AWS Glue, or comparable ETL/ELT frameworks.
• Strong understanding of data-quality dimensions including accuracy, completeness, consistency, uniqueness, validity, freshness, referential integrity, and distribution.
• Experience with defect analysis, release validation, and root-cause investigation.
• Ability to work directly with business SMEs to translate complex domain requirements into measurable test scenarios.
• Strong analytical and communication skills, including the ability to explain quality findings and risks to both technical and business audiences.

Preferred Qualifications:

Experience with several of the following would be valuable:

• dbt testing and custom tests
• Amazon MWAA / Apache Airflow
• AWS Glue and S3
• DataHub or similar metadata/catalog platforms
• Data profiling, anomaly detection, and statistical or distribution-based validation
• Data-quality scorecards and quality KPIs
• CI/CD integration for automated data testing
• Large-scale or high-volume data validation
• Experience with analytical, research, economic, financial, real-estate, or other complex domain datasets

You do not need to be an expert in every technology in our environment. We are looking for strong data-testing fundamentals, analytical depth, automation capability, and the ability to understand both the technical and business meaning of data.

Success means creating a data-quality capability where:

• Critical data sets have documented, repeatable, and risk-appropriate test coverage.
• Business definitions and expected outcomes are translated into measurable tests.
• Reusable test data and expected results cover critical scenarios and edge cases.
• Data pipelines are validated from source through published output.
• Regression and reconciliation tests identify unintended changes before publication.
• Critical quality failures prevent incorrect data from reaching downstream products.
• Recurring production issues become automated regression coverage.
• Data Engineers can use shared testing capabilities across projects.
• Business teams have greater confidence that published data reflects agreed definitions, methodologies and expected outcomes.

What's in it for you?

When you join CoStar Group, you'll experience a collaborative and innovative culture working alongside the best and brightest to empower our people and customers to succeed.

We offer you generous compensation and performance-based incentives. CoStar Group also invests in your professional and academic growth with internal training and tuition reimbursement.

• Our benefits package includes (but is not limited to):
• Comprehensive healthcare coverage: Medical / Vision / Dental / Prescription Drug
• Life, legal, and supplementary insurance
• Virtual and in person mental health counseling services for individuals and family
• Commuter and parking benefits
• 401(K) retirement plan with matching contributions
• Employee stock purchase plan
• Paid time off
• Tuition reimbursement
• On-site fitness center and/or reimbursed fitness center membership costs (location dependent)
• Access to CoStar Group's Employee Resource Groups
• Complimentary gourmet coffee, tea, hot chocolate, fresh fruit, and other healthy snacks

The final salary or hourly rate offered for this role will fall within the range set forth below based on a variety of factors, including but not limited to, geographic location, skills, and competencies.

Base Compensation: CAD $120,000-$150,000 (annually)

We welcome all qualified candidates who are currently eligible to work full-time in Canada to apply. However, please note that CoStar Group is not able to provide visa sponsorship for this position.