Sr. Data Quality Engineer

Affinity Solutions is the leading consumer purchase insights company, providing a complete view of U.S. and U.K. consumer spending through exclusive access to transaction data. They are seeking a Senior Data Quality Engineer to lead initiatives ensuring the accuracy and reliability of their enterprise data ecosystem, architecting data quality frameworks and collaborating with cross-functional teams.

Responsibilities:

• Establish and enforce enterprise-wide data quality standards, frameworks, and best practices aligned with organizational objectives
• Lead comprehensive data quality initiatives to monitor, validate, and enhance data accuracy, completeness, consistency, and timeliness across all systems
• Design and implement automated data quality validation, profiling, and anomaly detection mechanisms
• Define and track key data quality metrics and SLAs to measure and improve data reliability
• Implement data quality gates and validation checkpoints throughout the data lifecycle
• Develop and maintain comprehensive metadata repositories, data dictionaries, and data lineage documentation to ensure transparency and traceability
• Ensure strict adherence to data privacy regulations (GDPR, CCPA, HIPAA) and industry compliance standards
• Implement and enforce robust security measures including data encryption, masking, tokenization, and role-based access controls
• Participate in data governance committees and contribute to policy development
• Partner closely with data scientists, analytics engineers, platform engineers, and business stakeholders to deliver reliable, fit-for-purpose data solutions
• Provide technical mentorship and guidance to junior and mid-level engineers, fostering a culture of excellence in data quality
• Conduct code reviews and promote engineering best practices across the data organization
• Translate complex technical concepts for non-technical stakeholders
• Monitor and evaluate emerging technologies, tools, and methodologies in data quality, observability, and governance
• Identify opportunities for process optimization and technical debt reduction
• Lead proof-of-concept initiatives to validate new technologies and approaches
• Drive strategic recommendations to enhance data reliability, efficiency, and organizational data maturity

Requirements:

• Bachelor's degree in Computer Science, Information Systems, Data Engineering, or related technical field
• 5+ years of progressive experience in data quality engineering, data governance, or data platform engineering
• Demonstrated track record of implementing enterprise-scale data quality solutions
• Proven experience leading technical initiatives and mentoring engineering teams
• Expert-level proficiency in SQL and query optimization techniques
• Advanced programming skills in Python, including libraries for data processing (Pandas, NumPy, PySpark)
• Strong understanding of distributed computing frameworks (Apache Spark, Hadoop ecosystem)
• Deep expertise in data modeling methodologies (dimensional modeling, data vault, 3NF)
• Comprehensive knowledge of ETL/ELT design patterns and data integration strategies
• Hands-on experience with cloud platforms (AWS, Google Cloud Platform, or Azure)
• Proficiency with modern cloud data warehouses (Amazon Redshift, Google BigQuery, Snowflake)
• Experience with data quality and observability tools (Great Expectations, Soda Core, Monte Carlo, or similar)
• Familiarity with workflow orchestration tools (Apache Airflow, Prefect, Dagster)
• Knowledge of data cataloging and governance platforms (Datahub, Openmetadata, Alation, or similar)
• Version control systems (Git) and CI/CD practices
• Working knowledge of data privacy regulations (GDPR, CCPA) and compliance frameworks
• Understanding of data security principles and implementation of access controls
• Experience with metadata management and data lineage tracking
• Master's degree