Data Quality Lead

Data Quality Lead

About Us:

At retrain.ai, we're at the forefront of transforming the future of work using cutting-edge AI and machine learning. Our dynamic environment fosters innovation and collaboration, where your skills can make a real impact.

Role Overview:

We're seeking an expert Data Quality Analyst to join our Data and AI team. In this role, you will play a crucial part in ensuring the highest standard of data integrity and quality across our AI-driven platforms. You'll work closely with our Data Product Manager, Data Engineers, and Data Scientists to maintain and enhance standards in data accuracy, consistency, and reliability.

Responsibilities:

Collaborate on Data Quality Control Measures: Work alongside data engineers to define, execute, and refine data quality control measures.
Support Data Quality Analysis: Assist in analyzing data to identify trends, anomalies, and areas for improvement.
Own Data Quality Procedures: Help develop and maintain data quality standards and procedures for the work of the data scientists and data engineers.
Contribute to Data Quality Improvement Initiatives: Participate in projects aimed at data improvement, including identifying areas for improvement and possible solutions.
Monitor and Report Data Quality Issues: Establish and maintain a data monitoring and system, and contribute to the development of effective reporting mechanisms.
Engage in Cross-Functional Collaboration: Interact with various departments to understand their data needs and challenges.

Requirements:

Bachelor's degree in a related field (Data Science, Computer Science, etc.).
Strong analytical skills with attention to detail.
Familiarity with data quality concepts and methodologies.
Experience in data analytics, Python, query languages such as SQL, and data visualization
Understanding of databases and data structures.
Excellent communication skills to collaborate effectively with cross-functional teams.
Eagerness to learn and grow in a dynamic and fast-paced environment.

Advantages:

Exposure to data engineering or data science projects.
Knowledge of data quality/software quality tools and frameworks.