Skip to content
Cube

Data Quality Analyst

PondicherryLocation unclear
FullTime · Technology Operations
POSTED 2 SEPTVERIFIED 1D AGO
Apply Ask
Role
69 days opentrue age
Posted 2 Sept
CompanyCube →
14open roles
Opened · 30d
+0
Closed · 30d
−0
3d of snapshots · 30d window
Market
37,654open · Software engineering
Opened · 7d
+2,3546% of open
All software engineering
75,188open
All tracked boards · live 10 Nov · n=37,654
Visa
1H-1B LCAs · FY26
UK licensed sponsor
✓Worker (A rating)
LCAs FY26 · n=1 · to 2026-05-27

CUBE are a global RegTech business defining and implementing the gold standard of regulatory intelligence for the financial services industry. We deliver our services through intuitive SaaS solutions, powered by AI, to simplify the complex and everchanging world of compliance for our clients.

Why us?

🌍 CUBE is a globally recognized brand at the forefront of Regulatory Technology. Our industry-leading SaaS solutions are trusted by the world’s top financial institutions globally.

🚀 In 2024, we achieved over 50% growth, both organically and through two strategic acquisitions. We’re a fast-paced, high-performing team that thrives on pushing boundaries—continuously evolving our products, services, and operations. At CUBE, we don’t just keep up we stay ahead.

🌱 We believe our future is built by bold, ambitious individuals who are driven to make a real difference. Our “make it happen” culture empowers you to take ownership of your career and accelerate your personal and professional development from day one.

🌐 With over 700 CUBERs across 19 countries spanning EMEA, the Americas, and APAC, we operate as one team with a shared mission to transform regulatory compliance. Diversity, collaboration, and purpose are the heartbeat of our success.

💡 We were among the first to harness the power of AI in regulatory intelligence, and we continue to lead with our cutting-edge technology. At CUBE, You will work alongside some of the brightest minds in AI research and engineering in developing impactful solutions that are reshaping the world of regulatory compliance.

Purpose

The Data Quality Analyst plays a critical role in ensuring the organisation maintains accurate, complete, and reliable data that underpins the core Cube product, its golden data source, regulatory compliance, reporting, analytics, automated decisioning, and operational efficiency.

This role is responsible for identifying, investigating, and resolving data quality issues across key systems. This includes assessing data errors, determining root causes, implementing robust remediation actions, and applying controlled corrections to data and ingestion procedures in line with agreed governance standards, approval processes, and audit requirements. The Data Quality Analyst ensures that any data amendments are accurate, traceable, and completed in a controlled manner to protect the integrity of enterprise data.

Working closely with technology, product, and operational business teams, the Data Quality Analyst applies data governance standards, data quality rules, and analytical techniques to improve the accuracy, consistency, and usability of data. The role also supports the adoption of AI-enabled monitoring and anomaly detection tools to improve the speed and accuracy of data quality operations.

The data quality analyst will utilise analysis of existing ingestion processes and BAU processes to implement improvements that enhances the deliverables, removes routine manual work and reduces margin for error.

By improving the quality of data that underpins the Cube product and customer journeys, the Data Quality Analyst plays an essential part in enhancing customer experience, preventing downstream errors, and ensuring customers receive accurate, timely, and dependable services.

Key Responsibilities

• Profile data, design and execute data quality rules, and monitor scorecards and dashboards to identify issues, trends, and areas requiring corrective action.

• Investigate data anomalies and root causes using Excel, MSSQ and SSIS, assessing the impact of data issues and determining appropriate remediation steps against the production golden data source and LIVE customer facing databases

• Apply controlled corrections to data where required, ensuring changes are accurate, authorised, traceable, and completed in line with agreed governance standards, approval processes, and audit requirements.

• Design and implement improvements to the existing data ingestion pipelines and post load validation to increase efficiency and reduce risk of pipeline failures.

Source · CubeFirst seen 2 NovOriginal ↗