AI & Data Engineer
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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.
The role
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.
The type of person we need in this role
This role can only be done effectively by someone who:
- 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.