Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Analytics Manager - 6 month FTC

Datatech Analytics
City of London
1 week ago
Create job alert
Overview

Data Analytics Manager – 6 month FTC with cultural institution/NFP. 3 days office-based in Westminster, 2 days WFH. Salary £41,623 per annum + benefits. Job Ref J13008.

Candidates will need to have full working rights for the UK; the client is unable to consider a visa holder or sponsor a visa now or in the future.

This client is a well known cultural institute in London. They value audience insight and data-driven decision-making, supported by the Data & Insights Team which generates high-quality evaluation and analytics to guide engagement, product development, and strategic priorities. This role is required to strengthen their ability to deliver impactful insights, maintain the core reporting infrastructure, and manage the growing volume and complexity of requests across the institute.

The ideal candidate will have an analytical background, be proactive and delivery-focused, a good communicator able to work collaboratively and independently. Ideally with prior experience in the not-for-profit or cultural sectors.

Role and Responsibilities
  • Lead the development of dashboards and reports that support key performance tracking across audiences, digital engagement, and income-generating activity.
  • Translate complex data into clear, actionable insight for internal stakeholders through reports, briefings, and presentations.
  • Oversee the audience data pipeline and ensure high data quality, accuracy, consistency, and documentation.
  • Lead the delivery of forecasting and scenario modelling.
  • Maintain and enhance the client’s audience reporting infrastructure, working closely with IT and external providers.
  • Champion data governance, documentation, and responsible data use.
  • Manage relationships with external partners, particularly those responsible for managing the audience data warehouse.
  • Work closely with teams across Digital, Marketing, and Commercial Services to align analytics with organisational needs.
  • Provide expert input into data capability planning and contribute to cross-departmental digital transformation projects.
Experience & Qualifications
  • Significant experience in data analytics, including report/dashboard development and insight communication.
  • Strong technical and data modelling skills (e.g., SQL, Python or R). Proficiency in SPSS is desirable, but not essential.
  • Proficiency in visualisation tools (e.g. Power BI, Tableau).
  • Experience managing or working with cloud-based data infrastructure.
  • Strong communication and storytelling skills, with the ability to convey insight clearly to non-technical audiences.
  • Experience working cross-functionally and translating stakeholder needs into data products.
  • Familiarity with campaign/CRM analytics, GA4, or digital product performance.


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Analytics Manager

Data Analytics Manager

Data Analytics Manager

Data Analytics Manager

Data Analytics Manager

Data Analytics Manager

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.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.