Data Lakes Quality Engineering Specialist

We are seeking a skilled freelancer to assist with Data Lakes Quality Engineering. The role will support the continued development and execution of a best-in-class data infrastructure to enable a solid framework for data driven decision making, reporting & analytics and marketing automation. In this role, the ideal candidate will take ownership of the quality, reliability, and integrity of our data assets within the Databricks Lakehouse Platform. The ideal candidate will have experience in machine learning and data science, with strong English communication skills. Data Quality Framework & Strategy • Design and implement a comprehensive data quality framework specifically for our Databricks pipelines, leveraging the Medallion Architecture (Bronze, Silver, Gold). • Establish and enforce data quality standards, best practices, and validation rules tailored to the business logic and critical data elements. • Develop and maintain KPIs, metrics, and dashboards to track the health and quality of key datasets and pipelines over time. Implementation & Automation • Implement automated data quality checks and tests directly into our data pipelines using Databricks-native tools and associated technologies. • Leverage Delta Live Tables (DLT) with declared expectations (CONSTRAINT / ASSERT) to enforce data quality rules and manage pipeline failures. • Build and maintain data validation test suites using tools on Databricks, PySpark, or SQL. Qualifications • 5+ years of experience in data engineering, analytics engineering, or a dedicated data quality role. • Strong, hands-on experience with the Databricks Lakehouse Platform, including Spark, Delta Lake, and Databricks SQL. • Proficient in Python (especially PySpark) and advanced SQL for data manipulation and validation. • Proven experience implementing data quality frameworks and automated testing in a production data environment. • Experience with at least one major data quality or testing framework (e.g., dbt, Great Expectations, Deequ).