Data Quality Analyst

Rank’s Data Team is leading the way in delivering innovative data solutions, from building a cutting-edge data lakehouse, to mapping end to end data lineage and on to a highly personalised real-time customer solution we’ve got it covered. As the Data Quality Analyst you’ll be responsible for ensuring the data we feed into these systems is high quality and trustworthy.

The Data Quality Analyst’s role is focused on making sure our approach is prioritised, data quality checks are in place, reporting and remediation processes are established and the business stakeholders are engaged.

Working as part of a dynamic team in a regulated environment this role will be key in shaping the future approach to data quality at Rank. Core to this role will be….

• Build data quality approach – Prioritise, plan and deliver data quality rules across a wide range of systems.
• Deliver targeted reporting – Ensure clear and consistent data quality reports are available for both our technical and business stakeholders.
• Establish the remediation process – Build our approach to fixing data quality issues once they’ve been identified.
• Stakeholder management - Collaborate closely with business stakeholders, project teams, engineers, and testers to foster a culture of data excellence.
• Lead & facilitate action-oriented data working groups – drive practical solutions for impactful data quality improvements.
• Champion high-quality data: guarantee valuable data is well-managed, compliant, and delivers maximum value by identifying and unblocking issues.

Requirements

To excel in this role we’re looking for a candidate who has an effective combination of the following skills and experiences….

• Several years relevant data analysis or data quality management experience.
• Highly proficient with SQL, preferably with Python experience.
• Stakeholder-oriented with a passion for delivering exceptional solutions, able to present technical and non-technical information in appropriate terms to suit the audience and purpose.
• An understanding of data quality management principles.
• Experience with data quality tooling.