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For Companies At a Glance
- Tasks: Design and maintain scalable data pipelines for AI-driven applications.
- Company: Join an innovative AI tech company transforming HealthTech with cutting-edge solutions.
- Benefits: Enjoy a hybrid work model and competitive salary ranging from £80,000 to £110,000.
- Why this job: Work on impactful projects in a fast-paced environment with a focus on research-driven AI innovation.
- Qualifications: 3+ years in data engineering, strong ETL skills, and proficiency in Python and cloud platforms.
- Other info: Collaborate with cross-functional teams to drive data reliability and efficiency.
The predicted salary is between 64000 - 88000 £ per year.
Senior Data Engineer
London (Hybrid)
AI Technology
Salary: £80,000-£110,000
Paradigm Talent is currently working with an AI-driven technology company focused on building next-generation automation and intelligence systems for complex, high-stakes environments specifically within the HealthTech space.
The team applies advanced machine learning, computer vision, and multimodal AI to solve critical challenges in operational efficiency and decision-making. They work at the cutting edge of deep learning, object recognition, and large-scale AI systems, delivering solutions that drive real-world impact. If you're passionate about research-driven AI innovation and enjoy working on highly technical challenges, this role is for you.
The Role: Data Engineer (Scalable Data & AI Infrastructure)
We’re looking for a Data Engineer with experience in scalable data pipelines, cloud infrastructure, and real-time data processing. You will be responsible for designing, optimising, and maintaining secure, high-performance data architectures that support machine learning, analytics, and automation-driven applications.
This role offers the opportunity to work in a fast-paced, data-rich environment, collaborating closely with ML engineers, software developers, and product teams to ensure data reliability, security, and efficiency at scale.
What You’ll Do
Data Pipeline Development & Optimization
- Design, construct, and maintain large-scale data processing and ETL pipelines for structured and unstructured data.
- Optimize data flow, transformation, and storage, ensuring high efficiency and scalability.
- Develop and maintain data dashboards for real-time insights and analytics.
Cloud & Infrastructure Engineering
- Work with SQL/NoSQL databases and cloud data services (AWS) to manage and process large datasets.
- Optimize data warehousing, modeling, and indexing for performance and scalability.
- Leverage Apache Spark, Airflow, Kafka, or similar technologies to manage and automate workflows.
Data Security & Quality Control
- Ensure data security, compliance, and integrity, implementing best practices for access control and governance.
- Identify and resolve data quality issues proactively, ensuring clean, accurate, and usable data.
- Collaborate with machine learning and application engineering teams to prepare data for AI-driven applications.
Collaboration & Stakeholder Engagement
- Work closely with cross-functional teams, including ML researchers, software engineers, and business analysts, to understand data needs and optimize solutions.
- Support data collection and integration efforts, working with teams across multiple locations to ensure consistency.
- Bring an analytical mindset, ensuring that data-driven insights align with business and technical goals.
Skills & Experience
- 3+ years of experience in data engineering or a related field.
- Strong expertise in ETL development, building and maintaining scalable data pipelines.
- Proficiency in Python for data processing and automation.
- Hands-on experience with SQL/NoSQL databases and cloud data platforms (AWS)
- Understanding of data modelling, data warehousing, and database optimisation.
- Experience with distributed data processing tools (Apache Spark, Airflow, Kafka, or similar).
- Proactive approach to identifying and solving data quality issues.
- Strong project management skills, coordinating with cross-functional teams and data capture staff.
Senior Data Engineer employer: Paradigm Talent
Contact Detail:
Paradigm Talent Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Engineer
✨Tip Number 1
Familiarize yourself with the specific technologies mentioned in the job description, such as Apache Spark, Airflow, and Kafka. Having hands-on experience or projects that showcase your skills with these tools can set you apart from other candidates.
✨Tip Number 2
Network with professionals in the AI and HealthTech sectors. Attend relevant meetups, webinars, or conferences to connect with potential colleagues and learn more about the industry trends, which can give you valuable insights during interviews.
✨Tip Number 3
Prepare to discuss your experience with scalable data pipelines and cloud infrastructure in detail. Be ready to share specific examples of how you've optimized data flow and ensured data security in previous roles, as this will demonstrate your expertise.
✨Tip Number 4
Showcase your collaborative skills by highlighting past experiences where you worked closely with cross-functional teams. Emphasizing your ability to communicate effectively with ML engineers and software developers will illustrate your fit for the role.
We think you need these skills to ace Senior Data Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in data engineering, particularly with scalable data pipelines and cloud infrastructure. Use specific examples that demonstrate your expertise in ETL development and data processing.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for AI technology and your ability to tackle complex challenges. Mention your experience with tools like Apache Spark and AWS, and how they relate to the role.
Highlight Collaboration Skills: Since the role involves working closely with cross-functional teams, emphasize your collaboration and communication skills. Provide examples of past projects where you successfully engaged with ML engineers or software developers.
Showcase Problem-Solving Abilities: In your application, include instances where you've proactively identified and resolved data quality issues. This will demonstrate your analytical mindset and commitment to ensuring data integrity.
How to prepare for a job interview at Paradigm Talent
✨Showcase Your Technical Skills
Be prepared to discuss your experience with ETL development and scalable data pipelines. Highlight specific projects where you've successfully designed and optimized data architectures, especially in cloud environments like AWS.
✨Demonstrate Collaboration Experience
Since this role involves working closely with ML engineers and software developers, share examples of how you've collaborated with cross-functional teams. Emphasize your ability to understand diverse data needs and optimize solutions accordingly.
✨Prepare for Problem-Solving Questions
Expect questions that assess your proactive approach to identifying and resolving data quality issues. Be ready to discuss specific challenges you've faced and the strategies you employed to ensure data integrity and security.
✨Familiarize Yourself with Relevant Technologies
Brush up on your knowledge of distributed data processing tools like Apache Spark, Airflow, and Kafka. Be ready to discuss how you've used these technologies in past projects to manage and automate workflows effectively.
