Iain Dobson
Data Engineer, Quorum
Edinburgh, United Kingdom
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Iain is a seasoned IT professional with over two decades of experience. Based in an employee-owned IT consultancy in the heart of Scotland, he views his role more as a vocation than a job, as it continually presents new learnings and opportunities to meet interesting, intelligent people with shared interests.
Believing in the transformative power of IT, learning, and data, Iain advocates for ethical and accessible IT solutions to drive change. He combines technical proficiency with a passion for sharing knowledge with the IT community.
Area of Expertise
Topics
Using ChatGPT to accelerate your data project
ChatGPT has taken the world by storm with its ability to generate text, but it can also be used to assist your data project by helping design your database schema, write high quality SQL scripts, create Python code to link to external web services, and even design ADF dataflows. This session is designed to show you some of these key capabilities and how they can be used to accelerate your data project and also as a great learning tool for your own professional development.
Optimising Azure Architecture: A Deep Dive into Service and Private Endpoints
Knowing how to effectively implement Azure network services for security and performance requires a proper understanding of when to use Azure Service Endpoints and Private Endpoints.
This presentation provides an overview of each endpoint type and discusses the benefits and drawbacks of each. Attendees will learn how to make informed decisions on usage, avoid common pitfalls, and apply best practices for a secure Azure environment.
While the session delves deeply into the technical aspects, we will start from the basics, introducing networking components at a beginner level to ensure everyone can follow along. This approach ensures that even those new to endpoint security will grasp the foundational concepts before we explore the advanced topics.
Automating Data Quality: Great Expectations with a sip of SODA
Clean, reliable data is essential - not just for analytics and machine learning, but for trust in every data-driven decision. As data pipelines grow more complex, ensuring data quality consistently can become a major challenge.
This talk explores how to automate data quality checks using two powerful data validation tools - Great Expectations and Soda - with a focus on embedding checks into everyday CI/CD workflows using Azure DevOps.
We begin by examining the core drivers and challenges of data quality, including schema drift, data volume shifts, and integration friction. From there, we’ll explore each tool with live demonstrations, show how they validate assumptions, reduce risk, and foster transparency. You will learn how to migrate to and automate tests in Azure DevOps pipelines and close with a comparative overview of Great Expectations vs. Soda, and discussion on how automation promotes a collaborative data culture - making teams more confident and proactive.
What You’ll Learn:
* Key data quality risks and how they arise in real-world pipelines
* How to structure data validations using Great Expectations and Soda
* Practical techniques for embedding data checks into CI/CD workflows
* How to choose the right tool for your needs
* How automated data quality supports team empowerment and trust
Audience Relevance:
* Data Teams: Improve integrity and reproducibility of datasets.
* DevOps Practitioners: Add guardrails for better data reliability in deployments.
* Engineering Leaders: See how automation drives cultural and technical resilience.
Format: A mix of concise conceptual framing, hands-on demos, and platform-agnostic implementation examples using free-to-use tooling.
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