At a Glance
- Tasks: Build and optimise data pipelines while driving AI and Machine Learning initiatives.
- Company: Join a dynamic team at a leading data-driven organisation.
- Benefits: Remote work, competitive salary, and opportunities for professional growth.
- Other info: Collaborative environment that values diversity and continuous learning.
- Why this job: Make an impact by shaping the future of data analytics and AI solutions.
- Qualifications: Experience in Python, SQL, and data engineering with a passion for innovation.
The predicted salary is between 60000 - 80000 £ per year.
Reports to: Director of Data Science Engineering. We are looking for a Data Engineer with Machine Learning experience to join our team. They will be responsible for maintaining and supporting the existing data infrastructure used to underpin our data analytics and reporting. The Data Engineer will own the company’s drive into Machine Learning and AI adoption. They will be accountable for building as well as owning new Machine Learning and AI solutions across the team and the wider business. These must complement our current, scalable data architecture.
The Data Engineer will support the customer analytics function by developing and deploying machine learning models and techniques to deliver value around our consumer (B2C) data. They will be expected to optimize the architecture of our data pipelines and ensure that data flows support various cross-functional teams across the business. The ideal candidate will have a self-directed, innovative mindset who is comfortable supporting the data needs of multiple teams. The right candidate will be proactive in identifying and implementing improvements for our systems contributing constructively to the current data ecosystem.
As the business continues to invest in cloud solutions, particularly Google Cloud Platform, you will be excited by the prospect of owning new projects, propelling our data initiatives and capabilities. The Data Marketing department is the intelligence powerhouse of the business, representing the customer. Our goal is to inspire, engage and grow a loyal readership by harnessing data-driven insights and innovative marketing to deliver personalised, impactful content experiences that build lasting trust, diverse revenue streams, and a thriving future for quality journalism.
An accomplished ML/Data Engineer with proven experience deploying and managing ML and AI based services in production. Strong SQL skills and hands-on experience with both relational and non-relational databases, supporting data needs in fast-paced, content-driven environments. Strong expertise in designing and maintaining scalable data pipelines and architectures—integrating data from web analytics, content management systems (CMS), subscription platforms, ad tech, and social media.
Proven ability to automate and optimise data workflows, using modern ETL/ELT tools (e.g., Airflow, dbt, Apache Spark) to ensure timely and reliable delivery of data. Experience building robust data models and reporting layers to support performance dashboards, user engagement analytics, ad revenue tracking, and A/B testing frameworks. Skilled in cloud-based data platforms and infrastructure (e.g., AWS, GCP), ensuring scalability and security for large volumes of streaming and batch data. Additionally, experience with data warehouses such as BigQuery or Snowflake.
Adept in Python and/or Java for developing data services and integrating APIs to bring in diverse sources of media data. Exposure to utilising AI and Agentic workflows to build and deploy models agents and coding pipelines. Ideally having experience in Gemini, Claude and Vertex AI. Exposure to ML Frameworks such as PyTorch or TensorFlow. Understanding of ML Ops such as automated model testing, monitoring and tuning.
Excellent interpersonal and communication skills, enabling effective collaboration with analytical and commercial teams to turn data into actionable insights. Proactive and self-driven, capable of managing multiple data projects in a high tempo setting while meeting tight deadlines. A continuous learner with a diligent approach to data engineering including data privacy.
Design and Maintain Data Pipelines: Develop and maintain robust, scalable, and efficient data pipeline architecture to support current and future business needs. Identify, design, and implement improvements to automate manual processes, enhance data delivery performance, and re-architect infrastructure for improved scalability and resilience.
AI Agentic Workflows: AI adoption: Build on the current usage of AI across the business rolling out new processes and models. Machine Learning: Take the lead in building out our capability in this area with new models and recommender systems.
ETL Development and Infrastructure Building: Build and manage the infrastructure necessary for optimal ETL or ELT of data using Python, SQL, and Google Cloud Platform (GCP) big data technologies, such as BigQuery, Dataflow, Dataproc and Cloud Storage.
Business Intelligence Enablement: Prepare and transform pipeline data to support downstream analytics and feed BI tools (DOMO), enabling data-driven decision-making across the organization. Partner with internal stakeholders—ranging from Data, Commercial, and Editorial teams to executive leadership—to address data-related technical challenges and support their infrastructure needs.
Enhance Data System Functionality: Collaborate with the Data Team to continuously improve the functionality, flexibility, and performance of data systems and platforms. Data Governance and Compliance: Ensure all data is handled responsibly, securely, and in full compliance with the Data Protection Act, GDPR regulations, and the Company’s Code of Conduct.
SQL and Database Expertise: Strong working knowledge of SQL with hands-on experience querying and managing relational databases, alongside familiarity with a variety of database technologies (e.g., PostgreSQL, MySQL, BigQuery). Big Data Engineering: Proven experience designing, building, and optimizing ‘big data’ pipelines, architectures, and datasets, enabling efficient data processing at scale.
Skilled in performing root cause analysis on complex internal and external data sources and business processes to resolve issues and uncover opportunities for operational or strategic improvements. Unstructured Data Handling: Strong analytical capability for working with unstructured and semi-structured datasets, transforming raw information into actionable insights.
Data Workflow Development: Expertise in developing and maintaining data transformation processes, managing data structures, metadata, workload dependencies, and orchestration frameworks. Large-scale Data Processing: Project Management Collaboration: Excellent project management and organizational skills, with experience supporting and collaborating with cross-functional teams in dynamic and evolving settings.
Holds a graduate degree in Computer Science, STEM related quantitative field, with 2+ years of hands-on experience in a data engineering role. Databases: Proficient in SQL databases. Programming Languages: Skilled in one or more of the following languages, i.e.: Python, Java, Go.
Inclusive: We champion diversity in our teams and in our reporting. We take risks and are always looking to try new ideas in pursuit of excellence.
Data Engineer - Python - Remote employer: The Independent
Join a forward-thinking company that values innovation and collaboration, where as a Data Engineer, you will play a pivotal role in driving our Machine Learning and AI initiatives. Enjoy a supportive work culture that prioritises diversity and inclusion, alongside opportunities for professional growth in a dynamic remote environment. With access to cutting-edge technologies and a commitment to continuous learning, you'll be empowered to make a meaningful impact on our data-driven strategies.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer - Python - Remote
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data engineering projects, especially those involving machine learning and cloud platforms. This gives you a chance to demonstrate your expertise beyond just a CV.
✨Tip Number 3
Prepare for interviews by brushing up on common data engineering questions and practical scenarios. Practice explaining your thought process when solving problems, as this will highlight your innovative mindset and technical skills.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team at StudySmarter.
We think you need these skills to ace Data Engineer - Python - Remote
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Data Engineer role. Highlight your experience with Python, SQL, and any machine learning projects you've worked on. We want to see how your skills align with our needs!
Showcase Your Projects:Include specific examples of data pipelines or machine learning models you've built. We love seeing real-world applications of your skills, so don’t hold back on the details!
Be Clear and Concise:When writing your application, keep it clear and to the point. Use bullet points for easy reading and make sure to highlight your key achievements. We appreciate straightforward communication!
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. We can’t wait to hear from you!
How to prepare for a job interview at The Independent
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, especially Python, SQL, and Google Cloud Platform. Brush up on your experience with ETL tools like Airflow and BigQuery, as these will likely come up during the interview.
✨Showcase Your Projects
Prepare to discuss specific projects where you've built or optimised data pipelines or machine learning models. Be ready to explain your thought process, the challenges you faced, and how you overcame them. This will demonstrate your hands-on experience and problem-solving skills.
✨Understand the Business Context
Familiarise yourself with the company’s goals and how the Data Engineer role fits into their broader strategy. Think about how your work can contribute to customer analytics and AI adoption, and be prepared to share ideas on how to enhance their data systems.
✨Ask Insightful Questions
Prepare thoughtful questions that show your interest in the role and the company. Inquire about their current data challenges, future projects, or how they measure success in the Data Marketing department. This not only shows your enthusiasm but also helps you gauge if the company is the right fit for you.
