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Data Analyst

Posted 28 March by SES Water
Salary icon £28,000 - £32,000 per annum
Location icon Redhill , Surrey

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If you are a Data Analyst with a commitment to delivering high quality services to our customers, we’d love you to join SES Water on our journey to make a positive impact on the lives of those we serve and the environment we cherish. Together, we are shaping a sustainable future, one drop at a time.

A forward-thinking utility company, SES Water is driven by innovation and environmental responsibility. We continuously invest in cutting-edge technologies and sustainable practices to protect and preserve our precious water resources while meeting the evolving needs of our customers.

It’s an exciting time to join us as we seek to transform how data is used to enhance our service and be efficient. You will be joining a company where every colleague is valued for their contribution and encouraged to share their ideas, developing our experience and understanding of technology to improve our effectiveness.

What We Offer:

SES Water value our employee’s wellbeing and have created a package to care for both your financial needs and personal wellbeing.

  • Generous salary; up to £32,000 depending on experience.
  • Group personal pension plan with up to 10% employer contribution.
  • Life assurance for peace of mind.
  • Financial education, savings, and loans support.
  • Cycle to Work and Car Share Schemes and for eco-conscious commuting.
  • 25 days of annual leave for work-life balance.
  • Simply Health healthcare cashback scheme and Surgical Choices.
  • Service and MOT discounts at our on-site garage.
  • One paid day per year for volunteering in the community

As the Data Analyst, you will report into the Asset Intelligence Lead and will be responsible for the targeted analysis and interpretation of data related to leakage, providing hydraulic modelling support to inform decision-making and business reporting on asset performance.

Key Responsibilities:

  • Leakage targeting, through analysis of company district meter data. Working with in house and contract leakage detection staff, distributing tasks and keeping records of areas worked and leakage jobs raised.
  • Daily checks for incoming district meter data. Working alongside the Smart Asset Field Managers and the Smart Asset Data Technician to ensure data operability is maintained.
  • Assist with the upkeep of company’s asset databases.
  • Assist with the upkeep of the company’s software used for event detection and operational reporting.
  • Assistance with business plan analysis, supporting with hydraulic modelling tasks and working alongside strategic consultants to provide data and expert data insights that will inform our future decisions on investment.
  • Provide data analytical support to key business projects.
  • Support Company’s Incident Management Team in the role of Information Manager

Qualifications, Skills and Experience:

You will be educated to degree level (or equivalent) in maths, statistics or a science-based subject coupled with a genuine desire to progress and develop their career within the water industry.

The ability to interpret and analyse data is key as is familiarity with MS Excel and Word and maybe even MS Access, Power BI or Tableau.

Flexibility, strong prioritisation skills, initiative and self-motivation coupled with excellent written and verbal communication skills are also vital.

Application Process

The closing date for applications is the end of April so don’t worry if you don’t hear from us until then.

Diversity and Inclusion:

We thrive on the knowledge and life experiences of our colleagues, recognizing that our differences bring diverse perspectives and make us a great team. We welcome people who live our values, bring their true selves to work, and have a desire to share their lived experience to serve our communities both now and in the future.

Please let us know if you need any support during the application process.

Required skills

  • 1
    Data Analysis
  • 1
    Hydraulic Calculations
  • 1
    Modelling
  • 1
    Statistics

Reference: 52392910

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