About
We are a new generation consultancy based across UK and EU and founded on the premises of engineering excellence and empowering people to make an impact. We work with all modern tech stacks and typically run agile scrum on all our projects.
Responsibilities
•Develop, maintain, and optimise backend services for our SaaS platform
•Work with SQL and Python daily to build robust data pipelines and transformations
•Model, structure, and query data to support product features and reporting
•Integrate and maintain data ingestion flows (e.g., Fivetran, custom connectors)
•Implement backend APIs (FastAPI) to power our web application
•Work with our cloud environment (Azure - experience with AWS or GCP also valuable)
•Collaborate with teammates to plan, build, and deploy features in an Agile way
•Contribute to CI/CD workflows, monitoring, and infrastructure-as-code
Requirements
•Strong SQL and Python skills
•Data modelling experience (dimensional, relational, or similar)
•Experience building and maintaining backend systems
•Familiarity with Microsoft Azure
•Understanding of APIs, containers, and backend development best practices
•Implementing cloud-native data platforms
•Engineering scalable and reliable pipelines
•Good knowledge of distributed computing with Spark
•Understanding of cloud architecture principles and best practices
•Hands-on experience in designing, deploying, and managing cloud resources
•Agile ways of working
•Experience in cloud automation and orchestration using tools such as CloudFormation or Terraform
•Monitoring and performance tuning of cloud-based applications and services
•Some knowledge about how to architect, design, develop, deploy, and operate a data platform
Nice to have
•Experience with Snowflake, dbt, CubeJS
•Experience with Databricks, PySpark, ML workflows
•Experience with containerized deployments (Docker, Kubernetes, Azure Container Apps)
•Frontend development experience (React, Next.js, TypeScript)
•Infrastructure-as-code (Terraform, CloudFormation)
•Model Deployment & Serving – Deploy and manage ML models using MLflow, Azure ML, SageMaker, or similar, ensuring scalability and performance
•Monitoring & Retraining – Set up model drift detection, performance monitoring, and automated retraining
•ML Pipelines & CI/CD – Automate end-to-end ML workflows
Conditions
•Well-designed co-working and collaboration spaces
•Remote working patterns that work for you
•Parenting leave
•Sabbaticals and ability to work on personal projects
•Comprehensive employee wellbeing program including mental health support, flexible working arrangements, wellness activities, and a positive work culture
