Data Engineer ML and AI

Company Description

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.

Role Description

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.

Who We're Looking For

This role can only be done effectively by someone who:

1. Possesses 3-5 years of professional experience as a Data Engineer, with a significant focus on supporting Machine Learning and AI initiatives
2. Has proven experience in designing and building fault-tolerant data pipelines, including ETL.
3. Has hands-on experience supporting the operationalisation of machine learning applications.
4. Is proficient in Python and PostgreSQL
5. Has extensive experience with at least one major cloud provider (e.g., AWS, Azure, GCP) and their relevant data and ML services.
6. Has experience with data warehousing solutions (e.g., Snowflake, Redshift, BigQuery) and data lake technologies (e.g., S3, ADLS).
7. Has experience with Apache Spark (PySpark).
8. Is familiar with workflow orchestration tools (e.g., Airflow, Prefect, Dagster).
9. Is proficient with Git and GitHub/GitLab.
10. Has a strong understanding of relational, NoSQL and Vector databases.

Benefits

• 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