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- Tasks: Build and maintain AI-driven data pipelines while innovating our data infrastructure.
- Company: Join a leading research advisory firm focused on data-driven insights.
- Benefits: Enjoy hybrid work, professional development, enhanced pension, and 28 days annual leave.
- Other info: Collaborative culture with a focus on diversity, inclusion, and continuous learning.
- Why this job: Be at the forefront of AI innovation and make a real impact in data engineering.
- Qualifications: 4+ years in Data Engineering with strong AI/ML experience and Python skills.
The predicted salary is between 60000 - 80000 £ per year.
Our core asset is our data, and we are looking for a specialist who can not only maintain our high-standard data infrastructure while our Lead Data Engineer is on paternity leave but also accelerate our evolution into an AI-first organization. This role is a unique hybrid of stability and innovation. You will ensure our existing pipelines remain robust while leading the charge on AI improvements to our internal operations, systems, and client-facing products. 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 facets of this role
- AI Integration & Innovation: Design and deploy AI-driven features to automate internal operations and enhance our qualitative/quantitative research assets.
- Vector Infrastructure: Build and maintain vector databases and RAG (Retrieval-Augmented Generation) pipelines to unlock the value of our unstructured data.
- Pipeline Evolution: Transform existing ETL/ELT processes into AI-ready pipelines, ensuring data quality for machine learning training and inference.
- System Maintenance: Provide interim stewardship of our core data platform, ensuring uptime and performance while the Lead Data Engineer is away.
- Technical Mentorship: Act as the internal subject matter expert, upskilling the broader team on MLOPs and AI data best practices.
- Operational AI: Implement agentic workflows or automated insights to turn raw data into "AI-driven self-service" capabilities for our global clients.
The type of person we need in this role
- Experience: 4+ years in Data Engineering, with at least 2 years focused on AI/ML implementation (LLMs, NLP, or predictive modeling).
- AI Toolkit: Proven experience with Vector Databases (e.g., OpenSearch, CosmosDB, Milvus) and frameworks like LangChain or LlamaIndex.
- Core Engineering: Deep proficiency in Python and PostgreSQL.
- Big Data & Ops: Hands-on experience with Apache Spark (PySpark) and workflow orchestration (e.g., Airflow, Prefect, or Dagster).
- Cloud & Warehouse: Extensive experience with a major cloud provider (AWS/Azure/GCP) and modern warehouses like Snowflake, Redshift, or BigQuery.
- DevOps Mindset: Proficient with Git, CI/CD and the operationalisation of ML models (MLOps).
- Adaptability: The ability to step into a leadership gap, manage existing priorities, and pivot quickly toward innovation.
The qualities we’re looking for
- Problem-Solver: A proactive and analytical mindset, with the ability to diagnose and solve complex data and AI/ML infrastructure challenges.
- Collaborative & Enabling: Excellent communication and interpersonal skills, with a strong desire to teach, mentor, and share expertise effectively with Data Analysts, the Senior Data Engineer, and other stakeholders.
- Detail-Oriented: Meticulous attention to data quality, integrity, and pipeline robustness.
- Adaptable: Eagerness to learn new technologies and adapt to evolving ML/AI landscapes.
- Impact-Driven: A desire to contribute directly to the success of data-driven products and business outcomes, particularly in enabling new insights and self-service capabilities.
What we offer
- Strong professional development and continued learning.
- Hybrid work environment (2 days minimum in our London office) with core hours and time flexibility.
- Enhanced pension contributions.
- Annual profit share scheme.
- 28 days annual leave.
- Learning and development culture.
- Health helplines.
- Enhanced parental leave.
- Cycle to work scheme.
- Death in service insurance.
About us
Source is a research-led advisory firm that helps the world’s largest professional services firms make their most important decisions. With a wealth of independent insight, knowledge, and experience in the industry, Source delivers clear-cut direction that gives firms and their leaders the confidence to act.
As our AI & Data Engineer, you will be instrumental in enabling us to build robust, deep, and valuable data through advanced analytics and AI-driven capabilities.
Detailed role
- Build scalable data pipelines for ML and AI applications. Implement robust data ingestion strategies from diverse sources (e.g., databases, APIs, streaming services) and participate in designing efficient data transformation pipelines to prepare our qualitative and quantitative data for machine learning and AI consumption, setting standards for the team.
- Champion strategies for preparing diverse data into AI-ready features. Collaborate with Data Analysts to understand data needs for new insights and self-service tools, then lead the design and structuring of data appropriately for various AI and ML applications, guiding other engineers in these practices.
- Steer the cloud data platform's evolution to enhance AI/ML capabilities. Participate in optimising the usage of cloud-native data and ML services to ensure cost-efficiency, scalability, and high availability of the data platform, with a focus on AI/ML readiness, and advise the team on technology choices.
- Optimise data processing and ML pipelines for efficiency and scale. Proactively identify and resolve performance bottlenecks in data pipelines and ML workloads, ensuring optimal system efficiency, and sharing techniques for performance tuning with the team.
- Be the go-to expert for ML/AI data engineering best practices. Actively participate in technical discussions, conduct code reviews, and lead knowledge sharing sessions with Data Analysts, the Senior Data Engineer, and other engineering teams to foster a data-driven culture and elevate ML/AI understanding.
Diversity & Inclusion
At Source, we are committed to encouraging equality, diversity, and inclusion among our workforce, and eliminating unlawful discrimination. We are determined to ensure that no applicant or employee receives less favourable treatment on the grounds of gender reassignment, age, disability, religion or belief, sex, sexual orientation, marital status, or race, or is disadvantaged by conditions or requirements which cannot be shown to be justifiable. The aim is for our workforce to be truly representative of all sections of society and our customers, and for each employee to feel respected and able to give their best.
AI & Data Engineer in London employer: Source Information Services Limited
Contact Detail:
Source Information Services Limited Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI & Data Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to AI and data engineering. This gives you a chance to demonstrate your expertise and makes you stand out from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common technical questions and scenarios related to AI and data pipelines. 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, it shows you’re genuinely interested in joining our team at Source.
We think you need these skills to ace AI & Data Engineer in London
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter to highlight your experience in AI and Data Engineering. Use keywords from the job description to show us you understand what we're looking for!
Showcase Your Projects: Include specific examples of projects you've worked on, especially those involving AI/ML implementation. We love seeing how you've tackled challenges and made an impact in your previous roles.
Be Clear and Concise: When writing your application, keep it straightforward and to the point. We appreciate clarity, so avoid jargon unless it's relevant to the role. Make it easy for us to see your qualifications!
Apply Through Our Website: We encourage you to submit your application directly through our website. It’s the best way for us to receive your details and ensures you’re considered for the role without any hiccups!
How to prepare for a job interview at Source Information Services Limited
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technologies mentioned in the job description, like Python, PostgreSQL, and vector databases. Brush up on your knowledge of AI/ML frameworks and be ready to discuss how you've used them in past projects.
✨Showcase Your Problem-Solving Skills
Prepare examples of complex data challenges you've tackled. Be ready to explain your thought process and the steps you took to resolve these issues, especially in relation to AI integration and pipeline evolution.
✨Demonstrate Your Collaborative Spirit
This role requires excellent communication skills, so think of instances where you’ve successfully collaborated with others. Highlight your experience mentoring or teaching team members about MLOps and AI best practices.
✨Be Ready to Discuss Future Trends
Stay updated on the latest trends in AI and data engineering. Be prepared to share your thoughts on where the industry is heading and how you can contribute to making the company an AI-first organisation.