How to Ace an Azure Data Engineer Interview
By: Ron L'Esteve | Updated: 2020-07-01 | Comments | Related: More > Professional Development Interviewing
Navigating the turbulent waters of the Azure Data Lake and many of the other offerings within the Azure Data ecosystem can be a challenge for aspiring Azure Data Engineers. With all the various data services, coupled with multiple versions, pricing and security considerations, along with constant industry hype around buzzwords like 'Synapse', 'Databricks' and more, how can aspiring Azure Data Engineers prepare for an interview for the role of an 'Azure Data Engineer' to either begin or continue their career in the Azure Data Engineering space?
Azure is a complex ecosystem that brings with it the opportunity to specialize in a variety of areas ranging from DevOps to Security to Data. In this article, I will cover some of the fundamental requirements for aspiring Azure Data Engineers that are either new to the Azure data space or simply might be interested in picking up a few tips to help with acing an interview for an Azure Data Engineer position.
How to Prepare for an Azure Data Engineer Interview
This article will cover a few key tips for acing an upcoming Azure Data Engineer interview by empowering candidates to master the traditional Microsoft BI Stack and then understand Azure's Modern Enterprise Data and Analytics Platform, the various big data options and performance tuning recommendations, along with fundamental requirements of the Azure Data Engineer Associate certification. Finally, we will cover the value of expanding your knowledge across other Azure specialties to address the business value of an Azure data solution.
Master the traditional Microsoft Business Intelligence Stack
Many of Azure's Data Platform tools have its roots in the traditional Microsoft SQL Server BI Platform. For example, Azure Data Factory's Mapping Data Flows is much like SSIS, while Azure Analysis Services has its roots from SSAS. While Azure has many complexities that are not prevalent in the Microsoft's traditional BI Stack, having a strong understanding of SSIS, SSRS, SSAS, T-SQL, C#, Data Warehousing and more will help with having an enjoyable interview experience because there is a good chance that the interviewer and hiring organization has a very similar history of working with the traditional Microsoft BI Stack for many years and may be looking for an Azure Data Engineer to help with pioneering their journey into the Modern Enterprise Data and Analytics Platform. By having knowledge and experience in the traditional tooling which they currently have on-premises, you would be able to relate well to the employer and demonstrate a mastery of the traditional Microsoft BI Stack along with Data warehousing concepts, which will keep you at the top of the pile. For example, understanding how to design and implement a process to incrementally sync data from an on-premises SQL Database to Azure Data Lake Storage Gen2 may give you bonus points. Read Incrementally load data from a source data store to a destination data store for more insight into the incremental data movement process.
There are many online resources and books to help with mastering the traditional Microsoft BI skillset. Read 'The Data Warehouse toolkit' by Ralph Kimball for a thorough understanding of dimensional modeling. There are many other online resources for learning Microsoft BI ranging from paid courses to free YouTube tutorials.
Understand Azure's Modern Enterprise Data and Analytics Platform
While Microsoft Azure has a vast collection of resources, the most common components withing the Modern Enterprise Data and Analytics platform are listed in the diagram below. As an Azure Data Engineer, it will be critical to be able to design and implement an end to end solution that follows this architectural process or custom variations of it while accounting for security, high availability and more. It will also be critical to understand the differences and similarities between multiple data storage and data integration options. For example, see my article Choosing Between Azure Data Factory, SQL Server Integration Services, and Azure Databricks to learn more about how to choose the right ETL tool for the job based on certain use cases and scenarios. Another good comparison article related to Azure SQL Database versus Azure SQL Data Warehouse can be found in the following article: 'Is Azure SQL Data Warehouse a good fit'.
Finally, have a good understanding of recent trends, feature updates, availability releases and more of new and existing Azure data resources. Azure Updates is a great place to find many of these updates and you could filter the product categories to the data engineering specific resources. There are many other free learning resources available, ranging from articles to video tutorials on the keeping up with the latest and greatest in the Azure Data Platform.
Also see my other articles for more detail and step-by-step demos on designing and implementing Azure Data resources and solutions.
Understand how to manage Big Data with Azure
With all the hype around big data, along with the multiple big data products and resources available within Azure, the topic of big data is becoming increasingly important to many organizations. As an Azure Data Engineer, these organizations will be looking to you as their resident expert in the Big Data realm. Having a good understanding around the following will be key: big data architectures, tuning performance and scalability of ADF's copy activity, creating and configuring Databricks spark clusters, performance tuning ADF's Mapping Data flows, and fast ways to load big data into Azure Data Lake using Azure Data Factory.
Understand the fundamental requirements of the Microsoft Certified: Azure Data Engineer Associate
With close to 10 Microsoft Azure Certifications available for aspiring Azure Experts, it is clear that there is quite a lot to learn about Azure. The Data Engineer Certification path is most relevant for the Azure Data Engineer and consists of passing two exams: DP-200 (Implementing an Azure Data Solution) and DP-201 (Designing an Azure Data Solution).
When I had taken these exams, I spent around 80 hours to prepare for the exams and had 3 hours to complete each exam. Microsoft offers both online-free as well as instructor led training programs for to prepare for these exams. Additionally, courses offered by Pluralsight and Udemy may help with preparing for these exams. The exams can then be scheduled online and taken at a remote or on-site test center. With a total cost of $330 for the certification, it is well worth the cost of completing this certification since it lays the fundamental foundations to the Azure Data Engineering landscape. Additionally, for those individuals who work for Microsoft partners, the cost can be fully expensed and may even include a bonus payout for passing the exams, along with the glory of becoming a Microsoft Certified: Azure Data Engineer Associate which will help keep your resume at the top of the pile.
Here is Microsoft's learning path for the Azure Data Engineer which covers designing and implementing the management, monitoring, security and privacy of data using Azure Data Resources.
Expand your knowledge across other Azure Specialties
While the Azure Data Engineer role covers a lot, there are many other specializations within Azure including DevOps, Security, AI, Data Science, Azure Administration, Database Administration and more. While it would be nice to have specializations across these areas, it may take time to acquire this knowledge base. During an Azure Data Engineer interview, the interviewer may ask questions related to DevOps, CI/CD, Security, Infrastructure as a Code best practices, Subscription and Billing Management etc. As an Azure Data Engineer, it would be helpful to embrace Azure from a wholistic view beyond the fundamentals of the role.
With free online video tutorials along with Microsoft's vast knowledge base of documentation easily available, understanding the end to end architectural process and how it relates to connectivity, security, infrastructure as code, azure administration, DevOps CI/CD, billing and cost management will help answer related questions and this will instill confidence in the interviewer that since you may be their first Azure resource, you would be able to wear a few different hats initially as the collective team pioneers its journey into Azure.
That said, having a clear understanding of all sixteen components on the following diagram along with how it all ties together from an architectural standpoint will once again keep your resume at the top of that pile of finalists.
Be able to address the Business Value of the Azure Data Platform
The Azure Data Engineer role is a highly visible one since it empowers and challenges organizations to embrace digital transformation at scale. Frequently the Azure Data Engineer will be involved in conversations with C-level executives at the organization and may be tasked with contributing to Business Requirement Documents that cover cost, security, and how an Azure Solution brings true business value to the organization. Being able to speak about the business value of an Azure Data and AI digital transformation solution will earn you massive points during a final round interview.
Interestingly, Microsoft has a five-hour (8 module) course that covers how to 'Learn the business value of Microsoft Azure'. For more information on the business value of AI, see my previous article 'Realizing the business value of an AI driven strategy and culture'.
Additionally, for a wholistic understanding of how to monitor and control your Azure spending and optimize the use of Azure resources, complete the following Microsoft course: Control Azure spending and manage bills with Azure Cost Management + Billing.
- Explore Microsoft Azure's additional role-based certifications including AI Engineer, Data Scientist, Database Administrator, Azure Administrator, DevOps Expert, Security Engineer and Solutions Architect Expert for a wholistic expertise in Azure.
- Read about the Continuous integration and delivery in Azure Data Factory for more information on the different steps in the CI/CD lifecycle.
- For an understanding of the Big Data analytics trends, explore the new features of hybrid transactional and analytical processing (HTAP) capabilities by reading, 'What is Azure Synapse Link for Azure Cosmos DB?'
- Read about simplifying largescale Azure deployments by packaging key environment artifacts with Azure Blueprints
Last Updated: 2020-07-01
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