Lead Analytics Engineer – DBT, Snowflake, AWS

Redcliffe
2 months ago
Applications closed

Lead Analytics Engineer – DBT, Snowflake, AWS,

A highgrowth technology business (the next tech Unicorn??) is West London is looking for a Lead Analytics Engineer to help build and scale the next phase of its data and intelligence platform. This is a rare opportunity to join a company at a pivotal point in its growth, working directly on the core data models that power customer personalisation, product development and key commercial decisions.

The Role

As a Lead within the Analytics Engineering team, you will take ownership of the modelling layer and bring structure, clarity and best practices to an ambitious and growing data organisation.

You will work across the full lifecycle of data modelling: designing clean layers, implementing DBT transformations, building Snowflake models, optimising performance and helping to shape the foundations of a scalable analytics ecosystem.

You’ll also play a key leadership role, guiding Junior Analytics Engineers and supporting the Head of Data in raising technical standards and delivery quality.

What You’ll Work On

• Lead the design, build and maintenance of DBT models and analytics layers

• Working with Snowflake to create performant, scalable datasets

• Implementing testing, documentation and governance best practices

• Leading and supporting junior analytics engineers

• Bringing clarity to business logic through close stakeholder collaboration

• Managing incremental models and data flows within an AWS-based environment

• Contributing to the roadmap of a new data intelligence platform

Tech Stack

• DBT, Snowflake, AWS, ThoughtSpot, Shopify, SQL & Python

Experience in ecommerce or consumer-facing products is useful but not essential.

About You

You’ll thrive in this role if you:

• Have held a Lead role previously – leading a small team and technical leadership in DBT and Snowflake.

• Have strong DBT and Snowflake experience

• Enjoy owning models end-to-end and improving standards

• Are confident working directly with stakeholders to define business logic

• Have experience leading or mentoring engineering or analytics teams

• Are energised by high-growth, high-pace environments

• Want to work somewhere where performance genuinely accelerates your career

Culture & Progression

This business moves fast and looks for people who enjoy that pace. It’s not a traditional 9–5 environment, but high performance is met with high reward:

• Quarterly salary reviews

• Opportunities for rapid promotion

• Additional equity grants for strong performers

Hybrid working is preferred, with 2 days per week in the West London office.

West London (Hybrid – 2 days in the office per week)

£80,000–£90,000 + Equity + Quarterly Progression

If you’re a technically strong analytics engineer who has been in a Lead role previously and wants ownership, impact and progression in a scaling environment, we’d love to hear from you.

APPLY NOW for interview this week.

N.B. – They do not offer visa sponsorship

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.

How Many Data Engineering Tools Do You Need to Know to Get a Data Engineering Job?

If you’re aiming for a career in data engineering, it can feel like you’re staring at a never-ending list of tools and technologies — SQL, Python, Spark, Kafka, Airflow, dbt, Snowflake, Redshift, Terraform, Kubernetes, and the list goes on. Scroll job boards and LinkedIn, and it’s easy to conclude that unless you have experience with every modern tool in the data stack, you won’t even get a callback. Here’s the honest truth most data engineering hiring managers will quietly agree with: 👉 They don’t hire you because you know every tool — they hire you because you can solve real data problems with the tools you know. Tools matter. But only in service of outcomes. Jobs are won by candidates who know why a technology is used, when to use it, and how to explain their decisions. So how many data engineering tools do you actually need to know to get a job? For most job seekers, the answer is far fewer than you think — but you do need them in the right combination and order. This article breaks down what employers really expect, which tools are core, which are role-specific, and how to focus your learning so you look capable and employable rather than overwhelmed.

What Hiring Managers Look for First in Data Engineering Job Applications (UK Guide)

If you’re applying for data engineering jobs in the UK, the first thing to understand is this: Hiring managers don’t read every word of your CV. They scan it. They look for signals of relevance, credibility, delivery and collaboration — and if they don’t see the right signals quickly, your application may never get a second look. In data engineering, hiring managers are especially focused on whether you can build and operate reliable, scalable data systems, handle real-world data challenges and work effectively with analytics, BI, data science and engineering teams. This guide breaks down exactly what they look at first in your application — and how to shape your CV, portfolio and cover letter so you stand out.

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

Data engineering has quietly become one of the most critical roles in the modern technology stack. While data science and AI often receive the spotlight, data engineers are the professionals who design, build and maintain the systems that make data usable at scale. Across the UK, demand for data engineers continues to rise. Organisations in finance, retail, healthcare, government, media and technology all report difficulty hiring candidates with the right skills. Salaries remain strong, and experienced professionals are in short supply. Yet despite this demand, many graduates with degrees in computer science, data science or related disciplines struggle to secure data engineering roles. The reason is not academic ability. It is a persistent skills gap between university education and real-world data engineering work. This article explores that gap in depth: what universities teach well, what they consistently miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data engineering.