Data Platform Engineer

Zimmer Biomet
Leicester
3 weeks ago
Applications closed

Related Jobs

View all jobs

Principal Software Engineer - Data Platform

Bright Data Engineer Needed | London | SaaS | 1st Class STEM Degree

Bright Junior Data Engineers x 2 | London | SaaS Data Platform

Cloud Architect

Data Engineering Lead

Senior Data Engineer

At Zimmer Biomet, we believe in pushing the boundaries of innovation and driving our mission forward. As a global medical technology leader for nearly 100 years, a patient's mobility is enhanced by a Zimmer Biomet product or technology every 8 seconds.

As a Zimmer Biomet team member, you will share in our commitment to providing mobility and renewed life to people around the world. To support our talent team, we focus on development opportunities, robust employee resource groups (ERGs), a flexible working environment, location specific competitive total rewards, wellness incentives and a culture of recognition and performance awards. We are committed to creating an environment where every team member feels included, respected, empowered, and recognized.

What You Can Expect

The Senior Data Platform Engineer will be responsible for the development of data platforms which serve the needs of our development teams and data products that improve the quality-of-care and quality-of-life of Orthopaedic patients worldwide. The Connected Health AI and Data Science Team's existing platform is evolving from batch processing to cater for real-time and Generative AI solutions, the Data Platform Engineer will play a critical role in driving the growth and adoption of this platform.

How You'll Create Impact

  • Work closely with machine learning scientists and engineers to develop data serving platforms and platforms that power products on Microsoft Azure that process both real-time and batch data from diverse sources;
  • Integration of third-party platforms and software to build a platform for the efficient onboarding and manipulation of data;
  • Continuously identify areas for system improvements, focussing on enhacing both backend efficiency and user experience;
  • Develop re-usable architectures and infrastructure via Infrastructure as Code for use in current and future products;
  • Fulfilling regulatory commitments through the use of automation;
  • Contribute to the shaping of the technological roadmap to help progress the Connected Health team;



What Makes You Stand Out

Proficiency in the following tools:

  • Python for developing data pipelines;
  • Apache Spark for the ingestion and transformation of data;
  • SQL databases;
  • Data stores such as Azure Blob Storage, Azure Data Lake, S3, Azure Cosmos DB;
  • Infrastructure as code tooling such as Terraform, Pulumi, Bicep;
  • Git and CI/CD pipeline tooling;



Your Background

Experience/competency in the following areas:

  • Strong communication skills as this position works as part of a cross-disciplinary product team;
  • Programming with Python and packages associated with the data engineering workflow;
  • Awareness of machine learning techniques and their applications;
  • Apache Spark and Apache Airflow for ETL pipelines;
  • Developing applications to run on the cloud in a cloud-native way;
  • Data pipeline, application and infrastructure monitoring with tools such as NewRelic;
  • Familiarity with infrastructure concepts such as virtual machines and networking;
  • Communicating analyses, technical ideas, and their value to a range of audiences;
  • Ability to learn new technologies and methodologies;



Some experience in one of the following areas is beneficial, but not essential:

  • Data quality monitoring
  • Working with healthcare data;
  • Working with and deploying applications to Kubernetes, Managed Container Environments;
  • Working with Azure data tools such as Synapse or Fabric;
  • Delivering software and/or artificial intelligence/machine learning in regulated spaces;



Travel Expectations

This role is home-based and the team embraces a culture of remote-first. The team regularly meets once every fortnight in Central London, but individuals can decide in-conjunction with the rest of their team whether to meet others in the team more or less regularly depending on their circumstances.

This role works closely with team members based in the U.S. therefore, occasional evening meetings will be required. There also may be occasional travel to the U.S. for internal meetings, and also travel in UK/Europe to meet with customers.

EOE/M/F/Vet/Disability

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Quantexa Machine‑Learning Jobs in 2025: Your Complete UK Guide to Joining the Decision‑Intelligence Revolution

Money‑laundering rings, sanctioned entities, synthetic identities—complex risks hide in plain sight inside data. Quantexa, a London‑born scale‑up now valued at US $2.2 bn (Series F, August 2024), solves that problem with contextual decision‑intelligence (DI): graph analytics, entity resolution and machine learning stitched into a single platform. Banks, insurers, telecoms and governments from HSBC to HMRC use Quantexa to spot fraud, combat financial crime and optimise customer engagement. With the launch of Quantexa AI Studio in February 2025—bringing generative AI co‑pilots and large‑scale Graph Neural Networks (GNNs) to the platform—the company is hiring at record pace. The Quantexa careers portal lists 450+ open roles worldwide, over 220 in the UK across data science, software engineering, ML Ops and client delivery. Whether you are a graduate data scientist fluent in Python, a Scala veteran who loves Spark or a solutions architect who can turn messy data into knowledge graphs, this guide explains how to land a Quantexa machine‑learning job in 2025.

Machine Learning vs. Deep Learning vs. MLOps Jobs: Which Path Should You Choose?

Machine Learning (ML) continues to transform how businesses operate, from personalised product recommendations to automated fraud detection. As ML adoption accelerates in nearly every industry—finance, healthcare, retail, automotive, and beyond—the demand for professionals with specialised ML skills is surging. Yet as you browse Machine Learning jobs on www.machinelearningjobs.co.uk, you may encounter multiple sub-disciplines, such as Deep Learning and MLOps. Each of these fields offers unique challenges, requires a distinct skill set, and can lead to a rewarding career path. So how do Machine Learning, Deep Learning, and MLOps differ? And which area best aligns with your talents and aspirations? This comprehensive guide will define each field, highlight overlaps and differences, discuss salary ranges and typical responsibilities, and explore real-world examples. By the end, you’ll have a clearer vision of which career track suits you—whether you prefer building foundational ML models, pushing the boundaries of neural network performance, or orchestrating robust ML pipelines at scale.

Machine Learning Programming Languages for Job Seekers: Which Should You Learn First to Launch Your ML Career?

Machine learning has swiftly become a cornerstone of modern technology, transforming entire industries—healthcare, finance, e-commerce, and beyond. As a result, demand for machine learning engineers, data scientists, and ML researchers continues to surge, creating a rich landscape of opportunity for job seekers. But if you’re new to the field—or even an experienced developer aiming to transition—the question arises: Which programming language should you learn first for a successful machine learning career? From Python and R to Scala, Java, C++, and Julia, the array of choices can feel overwhelming. Each language boasts its own community, tooling ecosystem, and industry use cases. This detailed guide, crafted for www.machinelearningjobs.co.uk, will help you align your learning path with in-demand machine learning roles. We’ll delve into the pros, cons, and ideal use cases for each language, offer a simple starter project to solidify your skills, and provide tips for leveraging the ML community and job market. By the end, you’ll have the insights you need to confidently pick a language that catapults your machine learning career to new heights.