Data Analysts / QA

📋 Description

• Validate data accuracy across pipelines from legacy to new systems.
• Build reconciliation frameworks for legacy vs modern data pipelines.
• Execute acceptance tests before production deployment.
• Validate identity resolution accuracy and match rates.
• Develop automated regression tests for data quality.
• Profile data to detect anomalies and ensure reliability.

🎯 Requirements

• 3+ years in Data Quality, QA, or Data Analysis.
• Expert-level SQL for data validation and analysis.
• Hands-on experience with data quality frameworks (Great Expectations).
• Strong Python for data validation, automation, or analysis.
• Experience with data profiling, reconciliation, and validation.
• Experience building or maintaining test automation for data pipelines.
• Strong attention to detail with data accuracy and reliability.
• Cross-functional collaboration with engineering and analytics teams.

🎁 Benefits

• Certifications in AWS, Databricks, and Snowflake.
• Udemy Business access for courses to boost skills.
• English lessons to support professional communication.
• Travel opportunities to attend industry conferences and meet clients.
• Mentoring and development programs for career growth.
• Flexible working options to balance your life.