Data Engineer - BigQuery

SystemsAccountants
City of London
7 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer - BigQuery
Salary: £75,000-£80,000
Role type: Permanent
Location: London/Hybrid

Our client is seeking a skilled

Data Engineer

to take the lead in developing and optimising their data systems. This role will focus on SQL database application development, utilising Google BigQuery as the new platform for data management, and supporting the development of data analytics and visualisation tools such as PowerBI. The Data Engineer will be responsible for ensuring seamless data ingestion, transformation, and reporting, as well as working with key stakeholders to provide timely and actionable insights.

Main Responsibilities

Engineering :
Developing and maintaining business-critical data ingestion, analysis, and management systems.
Utilizing API and ETL methods to integrate with internal systems.
Building tools to minimize errors and improve customer experience.
Automating data development and governance to improve data health.
Designing procedures for system troubleshooting, capacity management, and scaling infrastructure.

Analytics :
Developing scripts and data outputs to automate reporting and visualizations.
Ensuring timely and easily translatable data delivery for business stakeholders.
Supporting customer segmentation and data clustering for insights into customer cohorts.
Owning and ensuring the security, governance, and usability of visualization tools like PowerBI.

Key skills for the Data Engineer include:
Strong teamworking and communication abilities.
Experience with scripting languages like SQL, Python, (SSIS/SSRS), and Powershell.
Familiarity with Google BigQuery.
Expertise in visualization tools such as PowerBI, Tableau, or Looker.
Excellent problem-solving abilities and attention to detail.
Ability to manage multiple projects with excellent organizational skills.

Please forward your CV to

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 Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.