Senior Data Analyst

The Lettings Hub
Peterborough
3 weeks ago
Create job alert

The Lettings Hub are a prop-tech company who provide technology driven products and services to manage lettings. We work with a national network of letting agents and provide a range of services to help our clients with the move-in process. This includes completing the necessary checks on potential tenants before they move into a landlord’s property, as well as a range of additional property insurance products and services to support the letting agent, tenant and the landlord.

Job Description

We are looking for a hybrid Senior Data Analyst and Data Engineer who will own the analytical foundations of the business. This is a hands-on role combining SQL engineering, data modelling, automation, and dashboard development. You will build and maintain the data pipelines and analytical layers that support product, operations, finance, and compliance across a multi-platform ecosystem.

This role suits someone who enjoys both analysis and engineering, who wants to shape how data flows through an organisation, and who is motivated by improving systems rather than maintaining static reporting.

You will design and optimise data structures, build automated workflows, integrate systems, and develop reliable analytical models used across the group. You will reduce manual work, consolidate reporting, and turn disconnected data sources into well defined, reusable datasets.

This is the core data role for the organisation. You will set standards, improve our tooling, and have ownership over how our data environment evolves.

This is a hands‑on technical role with future leadership potential with the scope to influence how our data stack evolves.

Analytics and Modelling
  • Build analytical layers and dashboards in our BI platform that serve as single sources of truth. (Currently Metabase)
  • Develop metric definitions and modelling logic used across multiple teams.
  • Provide deeper analysis of product, operational, and financial performance
  • Create reusable data models that support scalable analytics and automation.
  • Implement validation, monitoring, and alerting to ensure data reliability.
  • Design, build, and maintain ETL and ELT pipelines using SQL and automation platforms.
  • Optimise database queries, indexes, and schemas to improve performance.
Automation and System Integration
  • Replace manual reporting, especially Excel based processes, with automated workflows.
  • Build and maintain integrations between internal systems, BI tools, finance systems, and external APIs.
  • Improve data flow across the organisation by designing efficient, traceable automation paths.
Governance and Quality
  • Document data models, definitions, and lineage to maintain clarity and transparency.
  • Support compliance with GDPR, FCA requirements, and internal governance processes.
First 60 to 90 Days: Technical Priorities
  • Consolidate all finance reporting into accurate, consistent analytical models and automated dashboards.
  • Review manual reporting workflows and convert them into automated, repeatable processes.
  • Map data sources, identify key reliability issues, and produce a remediation plan.
  • Begin standardising core entities and metrics across systems and teams.
Communication and Collaboration
  • Able to communicate technical decisions clearly to engineering, product, and finance teams.
  • Comfortable owning problems end to end, from defining the requirements through to automated solution.
About the Team

As a Prop-Tech business, we are always looking to the future. What can we be doing next to ensure Letting Agents jobs are made quicker and easier, tenants have a smoother journey and landlords have piece of mind? So, although we have over 100+ years of industry experience across the team, we are not stuck in the past.

The only way to achieve this is having great people, but also a great environment at work. This is why we prioritise wellbeing and culture and ensure all of our colleagues have the space to share ideas and grow with the business. We have quarterly awards to celebrate together as a whole team, a free snack station for those afternoons only chocolate will help and monthly challenges set by the CEO that brings the competitive side out of all of us!!

If you think this is the type of environment you would thrive in, make sure to apply

  • Some travel to Peterborough and Nottingham required during onboarding phase and from time to time where required.


#J-18808-Ljbffr

Related Jobs

View all jobs

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Senior Data Analyst

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.