Lead Data Engineer

Experis
London
23 hours ago
Applications closed

Related Jobs

View all jobs

Lead Data Engineer

Lead Data Engineer (GCP)

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Job Title: Lead Data Engineer
Location: London (Hybrid)
Contract: 6 Months (Potential Extension)
Start Date: ASAP

About the Client
Our client is transforming their industry by replacing cigarettes with innovative, smoke-free alternatives. They are leveraging technology, data, and AI to drive a global shift toward a smoke-free world. This is a fast-paced, high-impact environment, perfect for candidates who are strategic, independent, and excited to work at the forefront of data and AI innovation

The Role
We are looking for a skilled Data Engineer to design, build, and optimize enterprise-scale data pipelines and cloud platforms. You will translate business and AI/ML requirements into robust, scalable solutions while collaborating across multi-disciplinary teams and external vendors.

As a key member of the data architecture you will:

Build and orchestrate data pipelines across Snowflake and AWS environments.
Apply data modeling, warehousing, and architecture principles (Kimball/Inmon).
Develop pipeline programming using Python, Spark, and SQL; integrate APIs for seamless workflows.
Support Machine Learning and AI initiatives, including NLP, Computer Vision, Time Series, and LLMs.
Implement MLOps, CI/CD pipelines, data testing, and quality frameworks.
Act as an AI super-user, applying prompt engineering and creating AI artifacts.
Work independently while providing clear justification for technical decisions.Key Skills & Experience

Strong experience in data pipeline development and orchestration.
Proficient with cloud platforms (Snowflake, AWS fundamentals).
Solid understanding of data architecture, warehousing, and modeling.
Programming expertise: Python, Spark, SQL, API integration.
Knowledge of ML/AI frameworks, MLOps, and advanced analytics concepts.
Experience with CI/CD, data testing frameworks, and versioning strategies.
Ability to work effectively in multi-team, vendor-integrated environments.Why This Role

Join a global, transformative initiative shaping a smoke-free future.
Work with cutting-edge cloud, AI, and data technologies.
Opportunity to influence technical and strategic decisions across enterprise data delivery.
Dynamic, innovative environment where your work has real business impact

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 Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

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.