AI Data Engineer

What matters most

• Medallion Architecture Implementation

  The role requires hands-on expertise in Databricks or Microsoft Fabric using medallion architecture. You must demonstrate the ability to structure data through bronze, silver, and gold layers to ensure it is clean, reliable, and ready for AI/ML model training and production.

• AI/ML Data Enablement

  A core priority is bridging the gap between raw enterprise data and AI/ML engineers. You will be responsible for feature engineering and creating high-quality training and evaluation datasets, requiring a deep understanding of how data structure impacts model performance.

• Robust ETL/ELT Pipeline Development

  You must design and maintain scalable, repeatable data pipelines. This involves integrating diverse sources—including Microsoft enterprise systems and APIs—while implementing rigorous monitoring, error handling, and lineage to ensure data reliability.

• Data Governance and Security

  Given the focus on enterprise data, you must apply strict access controls, privacy standards, and governance frameworks. You are expected to document data flows and transformations to ensure compliance and auditability across all AI and analytics solutions.

Likely challenges to think through

• Balancing Speed with Governance

  In an agile environment, there is often tension between the rapid iteration required for AI experimentation and the strict governance/security standards required for enterprise data. Prepare by discussing how you maintain documentation and auditability without slowing down the development lifecycle.

• Complex System Integration

  Integrating data across disparate Microsoft enterprise systems and cloud platforms can lead to data quality issues. Prepare by highlighting your experience in root cause analysis and troubleshooting to resolve data flow inconsistencies between source systems and AI environments.

• Adapting to AI-Assisted Workflows

  The role values experience with AI-assisted development tools like GitHub Copilot. You may be challenged on how you integrate these tools into your CI/CD workflows to maintain code quality and security while accelerating development.

Topics to prepare

• Medallion Architecture in Data Platforms

  The role specifically requires experience with Databricks or Microsoft Fabric using a medallion architecture (Bronze, Silver, Gold layers). Review the conceptual flow of data from raw ingestion to refined, business-ready datasets. Exercise: Sketch a data pipeline diagram showing how raw geo-data would be processed through these three layers, including where quality checks and schema enforcement occur.

• Data Governance and Security for AI

  The position emphasizes ensuring data is secure, well-governed, and reliable for AI/ML models. Review principles of data lineage, auditability, and access control within cloud environments. Exercise: Define a checklist for ensuring a dataset is 'AI-ready', focusing on how to document data limitations, lineage, and privacy compliance for sensitive enterprise data.

Skills to revise

• PySpark Data Transformation

  Strong PySpark skills are essential for processing large-scale data. Focus on efficient data manipulation, joining datasets, and handling complex transformations. Exercise: Write a PySpark script that reads a sample dataset, performs a transformation to clean null values, and saves the output in a Delta format, simulating a Silver-layer processing step.

• CI/CD and Git Workflows

  The role requires CI/CD and git-based deployment workflows for data pipelines. Practice managing code versions and automating deployments. Exercise: Create a simple GitHub Actions workflow file that triggers a linting or testing step for a Python-based data transformation script whenever code is pushed to a repository.

• SQL for Data Modelling

  Proficiency in SQL is required for data modelling and integration. Review dimensional modelling techniques (star/snowflake schemas) and normalization. Exercise: Design a SQL schema for a hypothetical geo-data project that links sensor readings to asset metadata, ensuring the model supports efficient querying for analytics and AI training.

• Demonstrate Medallion Architecture Expertise

  Prepare a technical walkthrough of a data pipeline you built using Databricks or Microsoft Fabric. Explain how you implemented Bronze, Silver, and Gold layers to ensure data quality and readiness for AI/ML model training.

• Showcase Python and PySpark Proficiency

  Prepare code snippets or a project summary demonstrating complex data transformations, feature engineering, or automation tasks using PySpark. Highlight how you optimized performance for large-scale datasets.

• Evidence of CI/CD and Governance Implementation

  Be ready to discuss your experience with Git-based workflows and CI/CD pipelines. Explain how you integrated data security, privacy, and auditability into your previous data engineering projects.

• Prepare for AI-Assisted Development Discussion

  Reflect on your experience using tools like GitHub Copilot or Copilot Studio. Be prepared to explain how these tools have improved your coding efficiency or assisted in troubleshooting and documentation tasks.

• Formulate Strategic Questions for the Interviewer

  Prepare thoughtful questions regarding Fugro's specific data challenges, such as: 'How does the team currently balance data governance with the need for rapid AI experimentation?' or 'What are the primary enterprise systems you are looking to integrate into the current data platform?'