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- Tasks: Join us as a Data Engineer to build and optimise 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, enhanced pension contributions, and 28 days annual leave.
- Why this job: Be at the forefront of AI innovation, transforming data into actionable insights and driving impactful change.
- Qualifications: 4+ years as a Data Engineer with expertise in ML/AI, Python, and cloud services required.
- Other info: Strong culture of professional development and continued learning awaits you.
The predicted salary is between 48000 - 72000 £ 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.
Strong professional development and continued learning culture. Flexible hybrid work environment with core hours 10-4. Enhanced pension contributions, 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 tools and technologies in ML and AI. Being well-versed in platforms like AWS, Azure, or GCP can set you apart, as these are crucial for the role.
✨Tip Number 2
Engage with the data engineering community online. Join forums or attend meetups focused on ML/AI to network and learn from others in the field, which can help you gain insights into best practices.
✨Tip Number 3
Showcase your hands-on experience with data pipelines and ETL processes. Be prepared to discuss specific projects where you've successfully implemented these skills during interviews.
✨Tip Number 4
Brush up on your Python and PostgreSQL skills, as these are essential for the role. Consider working on personal projects or contributing to open-source projects to demonstrate your proficiency.
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, especially focusing on your work with Machine Learning and AI. Use specific examples of projects where you've built data pipelines or optimised data processing.
Craft a Compelling Cover Letter: In your cover letter, express your passion for data engineering and AI. Mention how your skills align with the responsibilities outlined in the job description, such as your experience with cloud platforms and data warehousing solutions.
Showcase Relevant Skills: Clearly list your technical skills relevant to the role, such as proficiency in Python, PostgreSQL, and experience with tools like Apache Spark. Highlight any hands-on experience you have with machine learning applications.
Demonstrate Continuous Learning: Mention any recent courses, certifications, or projects that showcase your commitment to professional development in data engineering and AI. This will resonate well with the company's culture of continued learning.
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 any specific projects that highlight your expertise.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving skills in real-world scenarios. Think about how you would approach building scalable data pipelines or optimising data processing for efficiency, and be ready to articulate your thought process.
✨Emphasise Collaboration and Teaching
This role involves working closely with a team and teaching others. Share experiences where you've collaborated on projects or mentored colleagues, highlighting your ability to communicate complex ideas clearly and effectively.
