Data Analytics Engineer

TEC Partners
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst FTC

Data Analyst (Fixed Term Contract)

Databricks Data Engineer

Snowflake Data Engineer - 12month FTC

Head of Data Engineering

Data Analytics Engineer

Hybrid - London

Tec Partners have partnered with a specialist Machine Learning & Data consultancy that builds ML and Data products for companies across Europe. It was started by 2 experts in the field; the CTO has a PhD in ML, and he has built an incredible business based on problem-solving, collaboration, and ethics.

They are a small, passionate team of experts looking to grow their team. They're actively looking for a Data Analytics Engineer to join their London-based team.

You'll be working on a high-level project and dealing with large datasets for a business with hundreds of millions in turnover. This is the perfect role if you want to develop your coding skills and have a clear career progression path.

The ideal candidate for the Data Analytics Engineer:

1+ years' experience working as a Data Analyst or equivalent Experience using Tableau or PowerBI Good experience using SQL (Python is a plus but not essential) Pragmatic, problem-solving mindset

This role comes with advanced career and development opportunities from AAA mentors and experts and hybrid working in a beautiful office in London.

If this sounds like you, please apply with your up-to-date CV.

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.

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.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.