Senior Data Quality Engineer

Job Description 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.

Learn more about Zonda (

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

.

This position will be located in Toronto, Ontario and is in office Monday through Friday.

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.