Cloud Engineer NGO Job Natural State
IT Jobs, Natural State Jobs.
We are seeking a highly skilled Cloud Engineer to join our dynamic Conservation Technology team and lead the operationalisation of scientific and analytical processes that drive our impact.
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Responsibilities:
Principal areas of interest include:
- Design, deploy, and maintain robust and scalable data and ML pipelines using Azure cloud services and Databricks.
- Operationalise analytical/statistical models developed by Natural State’s Biometrics team, ensuring reproducibility, scalability, and cost-efficiency.
- Use dbt (Data Build Tool) to build and maintain modular, production-ready SQL-based transformation workflows.
- Develop automated workflows and CI/CD pipelines using GitHub Actions to enable continuous delivery of analytical models and metrics.
Collaborate closely with:
- Natural State’s Conservation Technology team to integrate analytical products into core technical systems.
- Natural State’s Biometrics team to translate analytical needs into scalable, cloud-native workflows.
- Lead the data engineering and orchestration aspects of analytical delivery: defining input data requirements, transformation logic, and output pipelines.
- Leverage Azure Machine Learning for model training, evaluation, and deployment where relevant.
- Apply Great Expectations to automate QA/QC checks and monitor data integrity and model performance.
- Support data governance and metadata management using Databricks Unity Catalog.
- Act as a bridge between scientific domain experts and engineering teams, ensuring that analytical intent is faithfully executed in technical implementation.
- Continuously evaluate and optimize system performance, cost, and maintainability.
- Contribute to web-based analytical tools by supplying APIs or output data that serve operational dashboards and reporting systems.
Requirements:
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, Cloud Computing, or related field.
- Demonstrated experience operationalising machine learning or statistical workflows in production environments.
- Proficiency in Azure, Databricks, and dbt is essential.
- Familiarity with Great Expectations, GitHub Actions, and Apache Spark.
- Experience with Azure Machine Learning and related ML lifecycle management.
- Solid understanding of data modelling, orchestration, and cloud-native architecture principles.
- Ability to independently translate scientific or statistical requirements into robust engineering solutions.
- Strong communication and collaboration skills to work across technical and non-technical teams.
- Experience working with tools like Terraform, Helm, and Kubernetes is advantageous but not required.
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How to Apply
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