Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Engineer

HeliosX Group
City of London
5 days ago
Create job alert
Overview

Ready to revolutionize healthcare, making it faster and more accessible than ever before?

How we started:

Back in 2013, our founder Dwayne D’Souza saw an opportunity to give people faster and more convenient access to medications using technology. We’ve grown rapidly since our inception, without any external funding whatsoever – achieving profitability through innovation and a highly disciplined approach to growth.

Where we are now:

We’ve earned the trust of millions of people worldwide through our top-selling products and well-known brands: MedExpress, Dermatica, ZipHealth, RocketRX, and Levity. A lot of our success is down to having our own pharmacies, manufacturers and products – spearheaded by leading in-house medical teams, researchers and pharmacists. Between 2023 and 2024 our global revenue tripled; £60m to £180m (300% year-on-year growth). We’re looking to do the same in 2025; move into new territories, and further accelerate our growth journey. There’s never been a more exciting time to join HeliosX.

Where we’re going:

Over the next five years, you’ll support our goal to become a world-leading healthcare partner, deepening our customer relationships, expanding into new countries, and diversifying our product portfolio to treat more conditions. You’ll be part of helping more people access prescription treatments and, most importantly, making personalised care better, quicker and easier for everyone. Come be a part of making our dream of easier and faster healthcare a reality!

About the role: The Data Engineer will be essential in the continuous development and operation of the data platform, focusing on building reliable data pipelines and ensuring data quality for both internal teams and external customer-facing products. You will execute on the data strategy and take responsibility for the implementation and maintenance of data solutions.

What you’ll be doing
  • Develop and Maintain Data Pipelines: Build and maintain core data processing workflows using dbt for transformations. This includes developing scalable SQL logic, creating reusable data models, and implementing incremental processing strategies following software engineering best practices (version control, testing, modular design).
  • Manage Cloud Data Platform (Snowflake): Configure and manage the Snowflake cloud data warehouse, focusing on optimizing query performance, controlling costs, and configuring compute resources. Ensure the platform scales effectively by implementing proper data clustering and partitioning strategies.
  • Ensure Data Quality and Testing: Implement comprehensive dbt testing frameworks (schema, data, and custom tests) and set up automated data quality monitoring, alerting, and issue resolution processes.
  • Establish Data Governance: Establish and enforce data governance policies, manage data access controls, and ensure security and privacy compliance.
  • Drive Cross-Functional Data Strategy: Collaborate with Engineering teams to design robust event schemas and instrumentation for consistent data collection at the source.
What you’ll bring
  • 2+ years of specific, proven experience delivering end-to-end data solutions using modern data stack tools.
  • 2+ years of expert SQL experience, with a focus on real-time or near-real-time data processing.
  • 1+ years of hands-on dbt experience building models, including those designed to feed customer-facing features and operational analytics.
  • Proven experience building data pipelines that feed directly into application databases or APIs, and a clear understanding of low-latency data requirements.
  • Experience building data products that surface in customer-facing UIs (e.g., dashboards, personalization). You understand API design and how analytics data integrates with the application layer.
  • Experience leading technical initiatives or mentoring junior team members
Why work with us?

At HeliosX, we want to improve healthcare for everyone, and to do this we need a team of brilliant people who share that ambition. We are currently a diverse team of engineers, scientists, clinical researchers, physicians, pharmacists, marketeers, and customer care specialists committed to our mission - but we need more talented folks to join us, if we want to achieve our global ambitions!

Aside from working with our all-star team, here are the other benefits of coming on board:

  • Generous equity allocations with significant upside potential
  • 25 Days Holiday (+ all the usual Bank Holidays)
  • Private health insurance, along with extra dental and eye care cover
  • Enhanced parental leave
  • Cycle-to-work Scheme
  • Electric Car Scheme
  • Free Dermatica and MedExpress products every month, as well as family discounts
  • Home office allowance
  • Access to a Headspace subscription, discounted gym memberships, and a learning and development budget (alongside a free Kindle and audible subscription)

Notes: This description contains standard job application details and is focused on responsibilities and qualifications for the Data Engineer role. No expired status indicated.


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

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 Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.