Salesforce Data Engineer (UK)

Intermedia
1 year ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Senior Data Engineer

Senior Data Engineer

SAS Data Engineer

Are you ready to make your mark?

About The Role:

We are seeking a skilled Data Engineer with expertise in Salesforce Data Cloud to design, develop, and maintain our data infrastructure. The ideal candidate will have a strong background in data engineering, cloud data platforms, and ETL processes. You will play a crucial role in ensuring the reliability, scalability, and performance of our data systems.

What you will be doing:

Design, implement, and optimize data pipelines and ETL processes Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and deliver high-quality data solutions Develop and maintain data models, schemas, and tables Ensure data quality, integrity, and security across all data processes Monitor and troubleshoot data pipelines, ensuring timely and accurate data delivery.

What you will bring to the role:

5+ years of experience in data engineering or a related role Strong experience using Salesforce Data Cloud Proficiency in SQL Strong teamwork and communication skills Experience working in an Agile environment Strong experience with ETL tools and processes Familiarity with data modelling, data warehousing, and big data technologies Experience with version control systems (e.g., Git) and CI/CD pipelines

Bonus Skills:

Experience in Snowflake, including data warehousing concepts, architecture, and best practices. Experience with Microsoft SQL Server Experience with other programming languages such as Python or Java Experience with data visualization tools (e.g. Power BI, MicroStrategy) Knowledge of data governance and data quality frameworks

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.

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

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

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

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.