Prudenceit Solutions Inc

We are seeking an experienced and meticulous Data Quality Engineer to ensure the reliability, accuracy, and completeness of our critical data assets. This role is essential for maintaining data integrity and building business trust by developing robust testing frameworks and implementing continuous data quality monitoring across our cloud-based data platform. The ideal candidate will possess deep expertise in SQL, Python, and key AWS data services, particularly AWS Glue, Amazon Redshift, and AWS Data Brew. Key Technical ResponsibilitiesI. Data Quality Framework Development & Automation Design, develop, and maintain end-to-end data quality frameworks using Python to automate testing, validation, and analysis of data pipelines and data warehouse tables. Build and implement custom data quality checks (e.g., uniqueness, completeness, validity, consistency, timeliness) and anomaly detection scripts within the automated framework. Integrate data quality checks directly into CI/CD pipelines to prevent poor-quality data from reaching production environments. Develop reporting mechanisms and dashboards to track and visualize key Data Quality Metrics (e.g., completeness, accuracy rates, latency, and compliance adherence) for business stakeholders.II. Data Platform Expertise & Implementation Leverage expertise in AWS Glue for building and implementing data transformation jobs and embedding data quality rules directly into ETL/ELT processes. Utilize AWS Data Brew for profiling, cleaning, and normalizing datasets, ensuring data readiness before consumption. Design and execute performance-optimized data quality checks and validation queries directly on Amazon Redshift data warehouse tables. Work with other relevant AWS technologies (e.g., S3, Lambda, CloudWatch) to build scalable and resilient data quality solutions.III. Data Analysis & Validation Write highly complex, efficient, and optimized SQL queries for in-depth data profiling, testing, validation, and root cause analysis of data quality issues. Perform deep-dive analysis on data sets to identify trends, patterns, and systemic data errors that impact business decision-making. Collaborate with Data Architects and Data Engineers to define and enforce organizational data governance and quality standards. Document data quality rules, validation logic, and data profiling results clearly and comprehensively.Required Technical Skills and Experience 7+ years of hands-on experience in a Data Quality, Data Engineering, or Data Testing role. Expert-level proficiency in SQL with proven experience writing complex queries for data analysis, validation, and manipulation. Strong programming skills in Python (or a similar language) focused on building testing frameworks, automation scripts, and data analysis utilities. Deep working knowledge of AWS data services, including: AWS Glue (Job Development, Data Catalog) Amazon Redshift (Querying, Performance Tuning) AWS Data Brew (Profiling, Cleaning) Proven ability to define, measure, and report on critical Data Quality Metrics (e.g., completeness, consistency, accuracy, uniqueness, validity, timeliness). Experience with data modeling concepts (e.g., star schema, snowflake) and their implications for data quality.