Solution Engineer

• Design and build robust data ingestion pipelines, transforming raw CSV/TSV data into structured, queryable datasets.
  
• Model custom entities and map diverse data sources into standardised target schemas.
  
• Build and manage data environments using SQLite, Google Cloud SQL and BigQuery.
  
• Develop and maintain Dockerised services for data loading, embedding generation and data-serving applications.
  
• Deploy and operate production workloads on Google Cloud Run, with a focus on scalability, reliability and high availability.
  
• Work across Redis, VPC networking, Artifact Registry and Cloud Storage.
  
• Implement secure cloud architectures using Cloud IAM, VPC and Cloud Run ingress controls.
  
• Automate data workflows and deployments through GitHub Actions, including scheduled ingestion, ID mapping, embedding generation and container builds.
  
• Build and integrate AI-powered capabilities, including natural-language querying, semantic search, embeddings and LLM-powered workflows.
  
• Contribute to emerging agentic architectures and integrations, including technologies such as Model Context Protocol (MCP).
  

Qualifications

• 2+ years’ experience in data engineering, cloud engineering or backend development.
  
• Strong Python development skills, including practical experience with Pandas.
  
• Solid experience with Docker and containerised applications.
  
• Strong working knowledge of Google Cloud Platform, particularly cloud compute, managed databases, data warehousing, networking and IAM.
  
• Strong SQL skills and experience working with both relational and analytical data stores.
  
• Experience designing, building and consuming REST APIs.
  
• A strong engineering mindset, with the ability to work independently, troubleshoot complex problems and take ownership from development through to production.
  

• Design and build robust data ingestion pipelines, transforming raw CSV/TSV data into structured, queryable datasets.
  
• Model custom entities and map diverse data sources into standardised target schemas.
  
• Build and manage data environments using SQLite, Google Cloud SQL and BigQuery.
  
• Develop and maintain Dockerised services for data loading, embedding generation and data-serving applications.
  
• Deploy and operate production workloads on Google Cloud Run, with a focus on scalability, reliability and high availability.
  
• Work across Redis, VPC networking, Artifact Registry and Cloud Storage.
  
• Implement secure cloud architectures using Cloud IAM, VPC and Cloud Run ingress controls.
  
• Automate data workflows and deployments through GitHub Actions, including scheduled ingestion, ID mapping, embedding generation and container builds.
  
• Build and integrate AI-powered capabilities, including natural-language querying, semantic search, embeddings and LLM-powered workflows.
  
• Contribute to emerging agentic architectures and integrations, including technologies such as Model Context Protocol (MCP).
  

Qualifications

• 2+ years’ experience in data engineering, cloud engineering or backend development.
  
• Strong Python development skills, including practical experience with Pandas.
  
• Solid experience with Docker and containerised applications.
  
• Strong working knowledge of Google Cloud Platform, particularly cloud compute, managed databases, data warehousing, networking and IAM.
  
• Strong SQL skills and experience working with both relational and analytical data stores.
  
• Experience designing, building and consuming REST APIs.
  
• A strong engineering mindset, with the ability to work independently, troubleshoot complex problems and take ownership from development through to production.