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Data Analysts (12-month FTCs)

Worldwide Salaried Open

Hiring Manager - Caroline Wright/Tamara Carpintero

Closing Date - 02.07.25

x3 FTC positions available, x1 involving travel

The Opportunity

Are you passionate about analysing data for a data transformation programme that drives real-world impact? We’re looking for Data Analysts to join our high-performing Data Engineering team, working on one of the most ambitious digital transformation programmes. This is a unique opportunity for driven, hands-on senior Data Analysts who thrives in a fast-paced, collaborative environment and is eager to make a tangible difference.

You’ll work closely with Data Engineers, Data Analysts, PMs, and stakeholders across the business, and customers, to analyse, interpret, monitor and visualise complex datasets — helping shape the future of land and property data in England and Wales.

This is a rare opportunity for highly skilled, energetic and motivated Data Analysts with deep hands-on expertise in data manipulation, with a strong SQL and geospatial background, and a strategic mindset — someone who can balance technical depth with delivery focus, stakeholder management and data analytical leadership.

In this role, you will:

  • Collaborate with cross-functional teams to understand data requirements and translate them into meaningful insights.
  • Analyse large, complex datasets to identify trends, patterns, and anomalies that inform business and technical decisions.
  • Build and maintain dashboards, reports, and visualisations that communicate insights clearly and effectively.
  • Support data quality initiatives by identifying inconsistencies, gaps, and opportunities for improvement, ensuring no data loss or duplication.
  • Contribute to the development of data models and pipelines in partnership with the Data Engineering team, and feeding into process and tooling improvements.
  • Present findings to technical and non-technical audiences, influencing decision-making at all levels.
  • As your first major engagement, you will play a key role in the HM Land Registry (HMLR) programme, expected to run through to the end of 2028, helping to centralise and modernise Local Authority Land Charge registers across England and Wales.
  • Support the overall data transformation of multiple Local Authorities – from source (digital and non-digital) into a common and defined schema on behalf of the end customer. This will involve working on multiple datasets from multiple LAs across the day.
  • Deal with tasks such as investigating and fixing dispersed geometries, automatizations, quality controlling data from manual sources, comparing pre and post transformation outcomes to ensure accuracy and other tasks to support a complex transformation process.

About You

We’re looking for energetic, curious, and detail-oriented analysts who are passionate about data and thrives on solving complex problems. You’ll bring:

  • Proven experience as a Data Analyst, ideally within a data-driven and engineering-led environments.
  • Strong analytical skills with the ability to work with large datasets and draw meaningful conclusions.
  • Proficiency in SQL and at least one data visualisation tool (e.g. Power BI, Tableau, Looker).
  • Experience with Python, R, or similar for data analysis is a strong plus.
  • Familiarity with databases. Also, cloud platforms (AWS preferred) is a plus.
  • Excellent communication skills — able to explain complex data concepts to non-technical stakeholders.
  • A proactive, self-starting attitude with a passion for continuous learning and improvement.
  • Experience with geospatial data or tools; QGIS is essential, ArcGIS and FME are desirable.
  • Familiarity with ETL processes and collaboration with data engineering teams is a strong plus.
  • Experience working in agile teams and contributing to iterative delivery cycles.
  • Advanced Excel skills
  • Openness to travel (expenses covered) to engage with Local Authorities on-site when necessary, supporting collaboration and insight gathering.

Originally posted on Himalayas

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