Data Engineer Advisor

What matters most

• Architecting AI-Ready Lakehouse Solutions

  The role requires designing modern Lakehouse architectures that integrate structured, semi-structured, and unstructured data. You must demonstrate how to build scalable platforms using Azure Synapse and Microsoft Fabric that specifically support advanced AI workloads like RAG and vector search.

• End-to-End Azure Data Pipeline Engineering

  You are expected to deliver production-ready, high-performance pipelines using ADF, Synapse, and ADLS Gen2. This includes implementing robust data quality frameworks, profiling, and discovery processes to ensure data integrity for downstream AI applications.

• Stakeholder Collaboration and Technical Translation

  As an Advisor, you must bridge the gap between business requirements and technical implementation. You will be evaluated on your ability to translate complex business needs into functional, scalable data solutions while maintaining CI/CD best practices in Azure DevOps.

Likely challenges to think through

• Integrating Traditional ETL with GenAI Workflows

  A significant challenge is balancing traditional data engineering (SQL/ETL) with the non-deterministic nature of GenAI pipelines (embeddings, vector search). Prepare by discussing how you manage data latency and quality when preparing data for vector databases versus traditional relational stores.

• Managing Diverse Data Modalities in Cosmos DB

  The requirement for NoSQL data modelling alongside traditional SQL environments suggests a need for hybrid architecture expertise. Prepare to explain how you optimize data models for both high-performance transactional access in Cosmos DB and analytical processing in Synapse.

Topics to prepare

• Modern Lakehouse Architecture

  Study the integration of Azure Synapse and Microsoft Fabric to create unified data platforms. Focus on how these architectures handle diverse data types (structured, semi-structured, and unstructured) to support AI-ready workloads. Exercise: Draft a high-level architecture diagram comparing a traditional data warehouse approach with a modern Lakehouse design for an AI-driven application.

• GenAI Data Pipeline Integration

  Understand the lifecycle of data for RAG (Retrieval-Augmented Generation) and vector search. This includes embedding generation, vector database storage, and retrieval mechanisms. Exercise: Review the workflow of ingesting unstructured documents into a vector store using Azure AI Search and identify the critical data quality checkpoints required for accurate LLM responses.

Skills to revise

• Azure Data Engineering Stack

  Demonstrate proficiency in building end-to-end pipelines using Azure Data Factory (ADF) and Synapse. Focus on orchestration, data movement, and transformation. Exercise: Create a sample ADF pipeline that triggers a Synapse notebook to process raw data from ADLS Gen2, ensuring error handling and logging are implemented.

• NoSQL Data Modelling

  Apply data modelling principles specifically for Azure Cosmos DB to ensure high performance and scalability. Exercise: Design a partition key strategy for a high-volume telemetry dataset in Cosmos DB, explaining the trade-offs between read and write performance based on your chosen partition key.

• CI/CD for Data Pipelines

  Implement automated deployment practices for data solutions using Azure DevOps. Exercise: Define a CI/CD pipeline configuration that automates the deployment of ADF ARM templates or Synapse workspace artifacts across development, test, and production environments.

• Data Quality and Profiling

  Utilize frameworks to ensure data reliability within pipelines. Exercise: Develop a Python-based script or a Synapse notebook routine to perform automated data profiling on a sample dataset, identifying null values, schema drift, and statistical anomalies.

• Architect a Lakehouse Solution

  Prepare a high-level architectural diagram demonstrating how you would integrate Azure Data Factory, Synapse, and Microsoft Fabric to support an AI-ready Lakehouse. Be prepared to explain your choice of storage formats and how you handle structured versus unstructured data ingestion.

• Demonstrate AI Pipeline Expertise

  Prepare a technical walkthrough of a RAG (Retrieval-Augmented Generation) pipeline you have built or designed. Focus on how you managed embeddings, vector search indexing in Azure AI Search, and the data quality checks required to ensure the accuracy of the retrieved context.

• Showcase NoSQL Data Modeling

  Prepare a case study or example of a complex NoSQL data model you designed for Azure Cosmos DB. Explain the partition key strategy, how you optimized for query performance, and how this model supported the specific business requirements of the project.

• Review CI/CD and Data Quality Frameworks

  Document your experience implementing CI/CD pipelines in Azure DevOps for data engineering projects. Include how you integrated automated data profiling and quality checks into the deployment lifecycle to ensure production-ready, high-performance data solutions.

• Formulate Strategic Questions

  Prepare questions for the interviewers regarding their current transition to Microsoft Fabric, the specific scale of their AI use cases, and how the data engineering team collaborates with data scientists to operationalize RAG and vector search models.