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A leading water services company is seeking a Meter Data Quality Analyst to ensure data integrity across multiple systems. This hybrid role involves monitoring data quality, validating assets, and presenting insights to stakeholders. The ideal candidate should have strong data analysis skills and experience in Advanced Excel, SQL, and Power BI. Competitive salary of £43,000 to £45,000 per annum with additional benefits included.
Thames Water is looking for a Meter Data Quality Analyst to join our Smart Metering Operations Centre (SMOC) team. We are rolling out hundreds of thousands of smart meters, generating millions of lines of data that provide vital insights into water consumption. This data helps our customers manage their water use more effectively, while supporting our ambition to make every drop count. We need skilled people who can manage, validate, and analyse data to unlock its full potential.
This is a varied and impactful role within the Smart Metering Operations Centre. Your work will ensure Thames Water gains maximum value from its smart metering investment by minimising errors and delivering accurate, reliable reporting. As our Meter Data Quality Analyst, you will work across two main areas, ensuring meter data integrity across multiple business units and IT systems.
To thrive in this role, the essential criteria you’ll need are:
Hybrid - Langley Bus Park - Slough, Berks
36 hours per week
We’re the UK’s largest water and wastewater company, serving more than 16 million customers. We aim to build a better future for all by helping customers, communities, people, and the planet thrive. We’re looking for passionate, skilled people who want to make a difference and help us reach our goals.
Working at Thames Water is a unique, rewarding, and diverse place to work with opportunities for fast-tracked career development, flexible working, and excellent benefits.
Disclaimer: due to high volumes of applications, we may close the advert earlier than the advertised date. We encourage you to apply as soon as possible.