ABOUT US

Syniti is the enterprise data partner, empowering anyone who relies on data to make business-critical decisions by delivering data they can trust through a unique combination of intelligent software and experts who deeply understand the role of data in enterprise growth.
Trusted by the Fortune 2000, Syniti helps leading businesses reconfigure the role of enterprise data from afterthought to foundational first step; enabling them to unlock valuable insights that ignite growth, reduce risk, and expand their competitive advantage.
Syniti’s Data First philosophy and enterprise data management platform supports data migration, data quality, data replication, data matching, master data management, analytics, data governance, and data strategy in a single, unified solution.
As an innovative, global leader in Enterprise Data Management, the combination of our award-winning software platform and premier consultants creates a unique advantage for leading enterprises. Syniti is also a preferred data solution used by the world’s top system integrators. Headquartered in Boston, Massachusetts with offices in 25 countries around the world, Syniti operates in all global regions and industry verticals, and maintains a 100% client success rate across thousands of complex data projects and initiatives.

ABOUT THE ROLE

As a Data Quality Manager, you’ll lead high-impact projects, guiding both clients and internal teams in diagnosing, improving, and maintaining data quality. You’ll shape best practices, coach analysts, and deliver solutions that create measurable business value.

WHAT YOU’LL DO

• Lead data quality assessments to analyse client datasets to identify quality issues.
• Lead, manage and coach Data Quality Analysts to effectively carry out their assignments.
• Using our unique Syniti data quality platform, connect to client systems, map their data landscape and profile their datasets.
• Work with our clients to define data rules and standards, define KPIs and then build data quality reports/dashboards to highlight their data issues.
• Work with them to understand the causes of their data issues and suggest improvements.
• Use AI tools to streamline the future data collection and analysis processes.
• Contribute to the development of the practice by improving our standard methodologies and processes.