Senior Data Quality Engineer

Job Description

Senior Data Quality Engineer – Zonda – Toronto, ON

Company Overview

CoStar Group (NASDAQ: CSGP) is a leading global provider of commercial and residential real estate information, analytics, and online marketplaces. Included in the S&P 500 Index, CoStar Group is on a mission to digitize the world’s real estate, empowering all people to discover properties, insights and connections that improve their businesses and lives.

We have been living and breathing the world of real estate information and online marketplaces for over 35 years, giving us the perspective to create truly unique and valuable offerings to our customers. We’ve continually refined, transformed, and perfected our approach to our business, creating a language that has become standard in our industry, for our customers, and even our competitors. We continue that effort today and are always working to improve and drive innovation. This is how we deliver for our customers, our employees, and investors. By equipping the brightest minds with the best resources available, we provide an invaluable edge in real estate.

About Zonda

Zonda is redefining the future of housing. We are perfectly placed in the heart of the fast-growing real estate industry. We are making big bets on the future of real-estate, trailblazing a 2030 vision for the industry. Here at Zonda, you’ll be able to use your passion and curiosity to drive the next generation of real estate analysts, advisors, technologists, and marketers.

Responsibilities

• 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 G