At a Glance
- Tasks: Design and maintain scalable data pipelines while driving AI and ML initiatives.
- Company: Join a leading media company focused on innovative data solutions.
- Benefits: Competitive salary, flexible working, and opportunities for professional growth.
- Other info: Dynamic team environment with a commitment to diversity and inclusion.
- Why this job: Be at the forefront of AI and ML, making a real impact in journalism.
- Qualifications: Experience in data engineering, SQL, and cloud platforms like GCP.
The predicted salary is between 60000 - 80000 £ per year.
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 solutions must align with business needs and be delivered with ROI in mind. They must complement our current, scalable data architecture.
They will support the customer analytics function by developing and deploying machine learning models and techniques to deliver value around our consumer (B2C) data. The Data Engineer 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. Lead Data Engineer will be exposed to best practice methods with the current framework.
Key Responsibilities and Accountabilities:
- Design and Maintain Data Pipelines: Develop and maintain robust, scalable, and efficient data pipeline architecture to support current and future business needs.
- Engineering and Integration: Assemble large, complex datasets from a variety of structured and unstructured sources, ensuring they meet functional requirements.
- Process Automation and Optimisation: 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: Design and deploy agentic workflows to increase efficiency in support of Engineering and the Analytics function.
- 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.
- Cross-Functional Collaboration: 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.
Skills and Experience:
- 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).
- 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.
- Strong analytical capability for working with unstructured and semi-structured datasets, transforming raw information into actionable insights.
- Expertise in developing and maintaining data transformation processes, managing data structures, metadata, workload dependencies, and orchestration frameworks.
- A demonstrated history of manipulating, processing, and extracting value from large, diverse, and disconnected datasets in fast-moving environments.
- 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.
- Proficient in SQL databases and experience with orchestration platforms such as Apache Airflow.
- Skilled in one or more programming languages, i.e.: Python, Java, Go.
- Strong understanding of cloud infrastructure such as GCP and tools within the respective platform.
Our values:
- Inclusive: We champion diversity in our teams and in our reporting. Working as a team, we put transparency and effective communication at the heart of everything we do.
- Innovative: From the very beginning, The Independent has been breaking the mould. We take risks and are always looking to try new ideas in pursuit of excellence.
- Independent: Nobody tells us what to think; we make up our own minds and aren't afraid to do things differently. Like our readers, we value honesty and integrity above outside influences.
ML Data Engineer in London employer: The Independent
At The Independent, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration. Our commitment to diversity, equity, and inclusion ensures that every employee feels valued and empowered to grow in their careers, while our investment in cutting-edge technologies like Google Cloud Platform provides unique opportunities for professional development in the rapidly evolving field of data engineering. Join us in shaping the future of journalism through data-driven insights and impactful content experiences.
StudySmarter Expert Advice🤫
We think this is how you could land ML Data Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people 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 projects, especially those involving machine learning and data engineering. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common technical questions and real-world problem-solving scenarios. Practice explaining your thought process clearly, as communication is key in collaborative environments.
✨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, we love seeing candidates who are genuinely interested in joining our team.
We think you need these skills to ace ML Data Engineer in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the ML Data Engineer role. Highlight your experience with data pipelines, machine learning, and cloud platforms like GCP. We want to see how your skills align with our needs!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Share your passion for data engineering and AI, and explain why you’re excited about joining our team at StudySmarter. Let us know how you can contribute to our mission.
Showcase Your Projects:If you've worked on relevant projects, don’t hold back! Include links or descriptions of your work with machine learning models, data pipelines, or any innovative solutions you've implemented. We love seeing real-world applications of your skills.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you’re serious about joining the StudySmarter family!
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 SQL, Python, and Google Cloud Platform. Brush up on your experience with ETL tools like Airflow and BigQuery, as these will likely come up during technical discussions.
✨Showcase Your Projects
Prepare to discuss specific projects where you've built or optimised data pipelines or machine learning models. Be ready to explain the challenges you faced, how you overcame them, and the impact your work had on the business. This will demonstrate your hands-on experience and problem-solving skills.
✨Understand the Business Context
Familiarise yourself with how data engineering supports the broader goals of the company. Think about how your role as a Data Engineer can drive AI adoption and enhance customer analytics. Showing that you understand the business side will set you apart from other candidates.
✨Ask Insightful Questions
Prepare thoughtful questions that show your interest in the role and the company. Inquire about their current data architecture, future projects involving AI, or how they measure the success of their data initiatives. This not only shows your enthusiasm but also helps you gauge if the company is the right fit for you.
