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- Tasks: Join us as a Data Engineer to build scalable data pipelines for ML and AI applications.
- 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, 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 specific tools and technologies mentioned in the job description, such as Apache Spark and PostgreSQL. Having hands-on experience with these will not only boost your confidence but also demonstrate your commitment to the role.
✨Tip Number 2
Engage with the data engineering community online, particularly around ML and AI topics. Participating in forums or attending webinars can help you stay updated on best practices and trends, which you can then discuss during your interview.
✨Tip Number 3
Prepare to showcase your previous projects that involved building scalable data pipelines or operationalising machine learning applications. Being able to articulate your past experiences clearly will set you apart from other candidates.
✨Tip Number 4
Network with current employees at Source or similar companies. This can provide you with insider knowledge about the company culture and expectations, which can be invaluable during the interview process.
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. Highlight specific projects where you've built data pipelines or supported ML applications, as this will demonstrate your hands-on expertise.
✨Understand the Company's Focus
Research Source's mission and how they leverage data for decision-making. Be ready to explain how your skills can contribute to their goals, particularly in transforming data into AI-ready assets.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving abilities in real-world scenarios. Think of examples where you optimised data processing or implemented fault-tolerant pipelines, and be ready to discuss the challenges you faced and how you overcame them.
✨Demonstrate Your Collaborative Spirit
Since you'll be working closely with a team, emphasise your ability to teach and enable others in ML/AI practices. Share experiences where you've collaborated effectively, showcasing your communication skills and teamwork.
