A little bit about the role

Over the past 18 months, The Dot Collective has successfully established a dedicated Data Architecture practice, laying the foundation for robust, scalable, and innovative data solutions. This practice underpins our commitment to delivering high-quality, data-driven outcomes for clients across diverse sectors. 

As we continue to grow, we are seeking a Data Solution Architect to play a pivotal role in shaping and implementing cloud native data and AI platforms that make a real impact.

Your Key Skills and Capabilities

• Work with technical teams to clarify and refine architecture so as to maintain consistency during deliver
• Recognise ways to describe the structure and behaviour of applications used in a business, with a focus on how they interact with each other and with business users or actors
• Help the customer articulate their requirements and formulate a suitable solution architecture
• Proven experience of designing and building data platforms. 
• A strong understanding of modelling data and how to make data work at scale
• A grasp of modern data governance is also appreciated
• Have an understanding of Microservices and Serverless architectures. 
• Be able to describe the structure and behaviour of the technology platform that underpins user applications and understand on-premise, Cloud hosting and end-user compute
• Knowledge of open source, open standards and cloud technologies
• An understanding of architectural concepts, methodologies and approaches
• Knowledge and demonstrable experience of both traditional and agile delivery methods
• Effectively explain complex technical solutions to a non-technical audience

Your key skills and capabilities: 

• 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  

Nice to haves:  

• 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 

Key tasks:  

• 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 

We expect you to have some knowledge about how to architect, design, develop, deploy, and operate a data platform.  

Your key skills and capabilities:

·      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
·      Excellent python and SQL skills
·      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

Nice to haves: (MLOps): 

• 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 

We expect you to have some knowledge about how to architect, design, develop, deploy, and operate a data platform. 

Your key skills and capabilities:

·      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
·      Excellent python and SQL skills
·      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

Nice to haves: (MLOps):  

• 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 

We expect you to have some knowledge about how to architect, design, develop, deploy, and operate a data platform.