Lead Data Engineer - Python, SQL, dbt, AWS, Dremio, Data Mesh, ETL

Cornwallis Elt
London
1 year ago
Applications closed

Related Jobs

View all jobs

Lead Data Engineer

Lead Data Engineer / Architect – Databricks Active - SC Cleared

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer – Python, SQL, dbt, AWS, Dremio, Data Mesh, ETL, Data Pipelines, Data Warehousing, DWH, S3, Glue, Data Lake, Automation, London

A Lead Data Engineer is sought after by a leading workplace pensions provider, to run their expanding data engineering team in their London City office. The team have recently built out a next-gen data lake hosted in AWS which makes use of Dremio, Data Mesh architecture and a wide range of AWS tools (S3, Glue, Step Functions etc.).
With the infrastructure of this new Data Lake in place, there is now a focus on enhancing the data stored within (via monitoring, cleaning and, as well as a requirement to design, build and implement robust ETL pipelines for effective integration with the wider business.

As a Lead Engineer, you will also be responsible for overseeing a small team of Data Engineers, setting and driving best practises and standards, ensuring project delivery is kept on track and regularly engaging with stakeholders up to C-suite for feedback/updates.
This will also involve working directly alongside the Head of Data Platforms to ensure ongoing engineering work is aligned to overall architecture and roadmap, as well as future project planning.

If you demonstrate the following skillset, please do apply!
• 7/8 years’ experience in Python programming, particularly around data analysis, automation and the building of data pipelines (as well as an understanding of data warehousing concepts)
• Strong SQL skills, especially in relation to ETL processes and data management (querying, cleaning, storing)
• Experience working with and deploying to AWS cloud (having worked with any/all of S3, Glue, Lambda, Step Functions etc.)
• Usage of PowerBI for data visualization
• Experience of both Dremio and Data Mesh Architecture
• Prior experience of leading software engineering teams (including line management)
• Clear, confident communications with previous experience of working directly with business users

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.