Data Quality Analyst

Key Responsibilities

• Design and implement data quality rules, checks, and metrics across multiple data sources and pipelines.
• Write advanced SQL queries to perform data validation, profiling, and anomaly detection.
• Develop and maintain Python scripts to automate data quality assessments and reporting.
• Apply AI/ML techniques (e.g., clustering, outlier detection, pattern recognition) to identify hidden data quality issues and inconsistencies.
• Collaborate with data engineers, analysts, and business stakeholders to understand data flows and ensure data integrity.
• Build and maintain data quality dashboards and alerts to monitor ongoing data health.
• Document data quality processes, findings, and remediation steps in a structured and accessible format.
• Participate in the development of data quality frameworks and contribute to continuous improvement initiatives.
• Support AI/ML model teams by ensuring high-quality input data and identifying potential data biases or gaps.
  

Required Skills & Qualifications

• Bachelor’s / master’s degree in computer science, Data Science, Information Systems, or a related field.
• 3+ years of experience in data quality, data analytics, or data engineering roles.
• Strong command of SQL for data analysis and validation.
• Solid understanding of Data Warehousing concepts (ETL, schema design, data lineage).
• Proficiency in Python for scripting, automation, and data manipulation.
• Exposure to AI/ML techniques such as anomaly detection, clustering, or supervised learning.
• Experience with data profiling tools and quality monitoring platforms.
• Familiarity with cloud data platforms (e.g., Azure , snowflake)
• Excellent analytical, problem-solving, and communication skills.
  

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