Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Join us as a Data Engineer to build and optimise data pipelines for ML and AI.
- Company: Source empowers firms with insights for strategic decision-making in critical situations.
- Benefits: Enjoy a flexible hybrid work environment, 28 days leave, and enhanced pension contributions.
- Why this job: Be part of an innovative team driving AI transformation and enhancing data capabilities.
- Qualifications: 4+ years as a Data Engineer with expertise in ML/AI, Python, and cloud services required.
- Other info: Opportunity for professional development and a profit share scheme.
The predicted salary is between 55000 - 75000 £ per year.
Source helps professional services firms understand what really matters when facing decisions of vital importance. We provide insights and resources to enable firms to make informed and strategic choices in critical situations. Our focus is on delivering valuable and actionable intelligence to help businesses achieve their objectives.
Join our innovative team at Source as a Data Engineer specialising in Machine Learning and AI. This critical role offers an exciting opportunity to help drive the technical direction and implementation of our ML/AI data engineering initiatives, transforming our existing data pipelines and qualitative/quantitative data into AI-ready and machine learning augmented assets. You will work closely with our Senior Data Engineer and the Head of Technology, to design, build, and maintain robust and scalable data infrastructure. You will assist in preparing our data for advanced analytics, visualisations and AI-applications. You will also be instrumental in teaching and enabling the broader data engineering team in ML/AI specific practices. The core asset of our business is our data, and you will be key to helping us extract new insights, provide deeper analysis, and enable AI-driven self-service capabilities for our internal and external users.
Key Responsibilities- Build scalable data pipelines for ML and AI applications.
- Champion strategies for transforming our diverse data for AI-driven capabilities.
- Identify tools and technologies to accelerate and enhance our delivery of data.
- Steer our cloud data platform's evolution to enhance AI/ML capabilities.
- Optimise data processing and pipelines for efficiency and scale.
- Be the team's go-to expert for ML/AI data engineering best practices.
This role can only be done effectively by someone who:
- Possesses 4+ years of professional experience as a Data Engineer, with a significant focus on supporting Machine Learning and AI initiatives.
- Has proven experience in designing and building fault-tolerant data pipelines, including ETL.
- Has hands-on experience supporting the operationalisation of machine learning applications.
- Is proficient in Python and PostgreSQL.
- Has extensive experience with at least one major cloud provider (e.g., AWS, Azure, GCP) and their relevant data and ML services.
- Has experience with data warehousing solutions (e.g., Snowflake, Redshift, BigQuery) and data lake technologies (e.g., S3, ADLS).
- Has experience with Apache Spark (PySpark).
- Is familiar with workflow orchestration tools (e.g., Airflow, Prefect, Dagster).
- Is proficient with Git and GitHub/GitLab.
- Has a strong understanding of relational, NoSQL and Vector databases.
- Salary range £55-75k
- Strong professional development and continued learning culture.
- Flexible hybrid work environment with core hours 10-4.
- Enhanced pension contributions.
- Annual profit share scheme.
- 28 days annual leave.
- Enhanced parental leave.
- Cycle to work scheme.
- Death in service insurance.
Data Engineer ML and AI employer: Source
Contact Detail:
Source Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer ML and AI
✨Tip Number 1
Familiarise yourself with the latest trends in Machine Learning and AI. Being able to discuss recent advancements or tools during your interview can demonstrate your passion and commitment to the field, making you a more attractive candidate.
✨Tip Number 2
Network with professionals in the data engineering and AI community. Attend meetups, webinars, or conferences where you can connect with others in the industry. This can lead to valuable insights and potentially even referrals for the position at Source.
✨Tip Number 3
Prepare to showcase your technical skills through practical examples. Be ready to discuss specific projects where you've built data pipelines or implemented ML solutions, as this will help illustrate your hands-on experience and problem-solving abilities.
✨Tip Number 4
Research Source's current data infrastructure and any recent projects they have undertaken. Understanding their specific challenges and goals will allow you to tailor your conversation and demonstrate how your expertise aligns with their needs.
We think you need these skills to ace Data Engineer ML and AI
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience as a Data Engineer, particularly focusing on your work with Machine Learning and AI. Use specific examples of projects you've worked on that demonstrate your skills in building data pipelines and optimising data processing.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role at Source and explain how your background aligns with their needs. Mention your proficiency in Python, PostgreSQL, and any cloud platforms you've worked with, as well as your experience with data warehousing and orchestration tools.
Showcase Relevant Projects: If you have a portfolio or GitHub repository, include links to relevant projects that showcase your expertise in ML/AI data engineering. Highlight any fault-tolerant data pipelines you've designed or machine learning applications you've operationalised.
Prepare for Technical Questions: Anticipate technical questions related to data engineering best practices, cloud services, and specific tools mentioned in the job description. Brush up on your knowledge of Apache Spark, workflow orchestration tools, and database management to impress during the interview process.
How to prepare for a job interview at Source
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python, PostgreSQL, and cloud platforms like AWS or Azure. Bring examples of data pipelines you've built and be ready to explain the challenges you faced and how you overcame them.
✨Demonstrate Your ML/AI Knowledge
Since this role focuses on Machine Learning and AI, brush up on relevant concepts and tools. Be ready to discuss how you've operationalised machine learning applications in the past and the impact they had on your projects.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving skills. Think about scenarios where you had to optimise data processing or transform diverse datasets for AI capabilities, and be ready to share your thought process and solutions.
✨Emphasise Collaboration and Teaching
This role involves working closely with a team and teaching others. Highlight your experience in mentoring or collaborating with colleagues on data engineering best practices, and how you can contribute to the team's growth.
