Job Title

Job Summary: We are seeking a skilled Data Quality Engineer to ensure the accuracy, reliability, and integrity of our data pipelines and workflows. The ideal candidate will have hands-on experience in data engineering concepts, with a strong focus on quality testing, validation, and pipeline orchestration. Key Responsibilities: Design, develop, and execute data quality test cases to validate data pipelines and ETL/ELT processes Monitor and trigger data pipelines, ensuring smooth execution and timely data delivery Run and maintain data quality scripts to identify anomalies, inconsistencies, and data integrity issues Perform data profiling and validation across multiple data sources and targets Collaborate with data engineers to implement data quality checks at various stages of the pipeline Perform root cause analysis (RCA) for data anomalies and pipeline failures Troubleshoot pipeline failures and data quality issues, working to resolve them efficiently Document data quality standards, testing procedures, and validation results Generate data quality reports and communicate findings with engineering teams Develop automated testing frameworks to improve data quality validation efficiency Focus primarily on validating and assuring quality of existing pipelines (not building full pipelines) Required Technical Skills: Strong understanding of data engineering concepts including ETL/ELT processes, data warehousing, and data modeling Proficiency in SQL for complex data validation and querying Experience with scripting languages such as Python or Shell scripting for automation Hands-on experience with data pipeline orchestration tools (e.g., Apache Airflow, Azure Data Factory, AWS Glue) Knowledge of data quality frameworks and tools (e.g., Great Expectations, Deequ, custom validation scripts) Familiarity with cloud platforms (AWS, Azure, or GCP) and their data services Understanding of data formats (JSON, Parquet, Avro, CSV) and data storage systems Exposure to logging/monitoring tools (CloudWatch, Datadog, ELK, etc.) is a plus Preferred Skills: Experience with big data technologies (Spark, Hadoop, Kafka) Knowledge of CI/CD practices for data pipelines Familiarity with version control systems (Git) Understanding of data governance and compliance requirements Experience with data visualization tools for quality reporting Soft Skills: Strong analytical and problem-solving abilities Excellent attention to detail Good communication skills to collaborate with cross-functional teams Ability to work independently and manage multiple priorities