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.