• The Data Generalist is an individual contributor in the Data team, operating across the full data lifecycle. In a small, highly autonomous team, this role focuses primarily on data engineering and backend engineering, with some involvement in data science and analytics engineering, emphasizing end-to-end delivery, stakeholder impact and continuous learning. 
• Data Engineering & Backend Engineering 
• Maintain and improve the data infrastructure using Snowflake, AWS, Terraform and Kubernetes, always keeping in mind reliability, security and cost 
• Orchestrate, refactor and troubleshoot
• ETL pipelines in Airflow 
• Develop FastAPI microservices that expose the data products, from simple reports to advanced rules-based and ML systems 
• Monitor data quality alerts and raise occurring issues to other development teams 
• Collaborate with the Head of Data on architectural decisions and technical improvements 
• Analytics Engineering & Data Modelling
• Build and maintain analytics models using dbt, following best practices such as star schemas and dimensional modelling 
• Adapt ETL pipelines to evolving data models 
• Ensure analytical datasets are reliable, well-documented, and optimised for self-service BI and downstream consumption 
• Occasionally work on ad-hoc analyses and customer-facing dashboards 
• Machine Learning & Advanced Analytics 
• Design, build, and productionise machine learning models 
• Develop user-facing ML and AI products and proof-of-concepts using tools such as scikit-learn, LLMs, Streamlit and FastAPI 
• Collaboration & Mentorship
• Actively collaborate with other Data team members through pairing sessions and informal mentorship 
• Share best practices across data, backend and ML engineering 
• Reach out to other technical leads to clarify backend logic, data semantics, or system behaviour required for data initiatives 
• Ownership & Stakeholder Interaction
• Lead data projects end-to-end with minimal supervision, from requirements gathering to delivery 
• Proactively spot opportunities for improvements in the data infrastructure and ML/AI adoption 
• Understand and articulate trade-offs between technical solutions, delivery speed, and stakeholder needs 
• Translate complex technical concepts into clear, business-oriented language 
• Occasionally present and explain data products, insights, and deliveries directly to stakeholders 

Requirements 

• 3+ years of experience in a data-related role 
• Proficiency in Python, SQL, dbt and Airflow 
• Experience building, maintaining, and documenting APIs in Python (preferably FastAPI) 
• Familiarity with modern DevOps practices – Cloud (AWS preferred), IaC (Terraform), CI/CD, Kubernetes 
• Familiarity with data science/ML concepts 
• Strong curiosity and willingness to learn 
• Good understanding of data tooling landscape, ability to pick a right tool for the job while keeping a pragmatic mindset 
• Nice to have: Some experience with LLMs/Agentic AI, Streamlit, Frontend development