Data Quality & Transformation Analyst (SQL / Testing)

Key Responsibilities:

Understand business and technical use cases and translate requirements into appropriate data transformations.

Design and develop SQL views and queries for analytics and reporting consumption.

Support schema design decisions (tables, relationships, data modeling considerations) to improve data usability and maintainability.

Develop test cases covering unit, integration, and regression testing for data pipelines and transformed datasets.

Create data quality checks to flag exceptions, anomalies, and outliers (e.g., missing values, unexpected distributions, referential integrity issues, threshold breaches).

Proactively identify potential data challenges (data drift, ingestion issues, schema changes, duplicates, late-arriving data, inconsistent formats) and implement preventive validations.

Collaborate with data engineers and analytics stakeholders to troubleshoot data issues and improve monitoring and data controls.

Maintain documentation of transformations, checks, and testing coverage to ensure transparency and repeatability.

Work with Tableau - Knowledgeable of dashboard performance optimization techniques

Experience leading data visualization approaches for projects, developing innovative approaches, and enterprise dashboard rollouts and adoption

Requirements:

Bachelor’s degree in Computer Science, Information Technology, Data Science, Engineering, or a closely related field.

Strong SQL skills, including writing complex queries and designing efficient views.

Solid understanding of data transformation concepts and how to adapt data for different analytical use cases.

Experience with data testing approaches, including designing regression, unit, and/or integration tests for datasets.

Knowledge of data modeling and schema design concepts (normalization/denormalization basics, keys/relationships, data types, constraints).

Ability to design robust data validation rules and exception/outlier detection logic.

Strong analytical thinking and attention to detail, with a proactive mindset toward preventing data quality issues.