As businesses continue moving to the cloud, the demand for Azure data engineers continues to grow. Azure data engineers design and implement data solutions on Microsoft’s Azure cloud platform. They build scalable systems that collect, store, process and visualize data in the cloud.
If you’re interested in becoming an Azure data engineer, there are some key skills you’ll need to develop. Let’s discuss the top skills and qualifications for this role.
Cloud Computing Skills
Since Azure data engineers work specifically with Microsoft’s cloud platform, having general cloud computing skills is essential. You should understand core concepts like IaaS vs PaaS, public vs private cloud, cloud security, and cloud architecture patterns. Hands-on experience using public cloud platforms like AWS, GCP or Azure will also be very valuable.
Azure-Specific Skills
While general cloud skills are important, you’ll want to focus especially on Azure. Gain practical experience with:
- Azure portal and account management
- Azure compute services like Virtual Machines, Azure Functions and App Service
- Azure storage services like Blob Storage, Disk Storage and File Storage
- Azure networking services like Virtual Networks, VPN Gateway, Load Balancer and Application Gateway
Knowledge of Azure DevOps, Azure Active Directory, and Azure governance best practices will also be helpful.
Data Engineering Skills
Since the “Data Engineer” portion of the job title refers to working with data, you’ll need strong data engineering skills. This includes:
Data Modeling
You need to be able to model data in both relational (SQL) and non-relational (NoSQL) systems. Understand normalization, keys, relationships, and table structures for relational databases. For non-relational systems, know how to denormalize data and design for scalability and flexibility. Hands-on experience with relational databases like SQL Server and non-relational stores like Azure Cosmos DB is a plus.
ETL Processing
Building ETL (extract, transform, load) pipelines is a core responsibility. Know how to connect to disparate data sources, transform data into a consistent format, cleanse data, and load it into a destination. Azure Data Factory is the main Azure service for this, so hands-on experience is valuable.
Querying and Analysis
Whether data is stored in a relational database or a data lake, you need querying skills to analyze and extract insights. SQL skills are essential. Experience with querying languages like Kusto for Azure Data Explorer is helpful. Understanding OLAP vs OLTP workloads is important too.
Stream Processing
For real-time data scenarios, understand concepts like messaging, event streams, and materialized views. Know how to implement stream processing patterns like lambda architecture. Azure services like Event Hubs, Stream Analytics and Time Series Insights are commonly used.
Data Pipeline Orchestration
You need to be able to orchestrate and schedule complex pipelines that move data between various storage systems, processes, and visualization tools. Know how to monitor pipelines and troubleshoot failures. Experience with workflow orchestration tools like Azure Data Factory is highly desired.
Data Design Skills
Data engineers don’t just move data around – they also design data storage architectures. Key skills include:
Data Warehousing
Understand common data warehouse designs like the Kimball star schema. Know concepts like facts, dimensions, ETL, and slowly changing dimensions. Hands-on experience building data warehouses on platforms like Azure Synapse Analytics is a plus.
Data Modeling
Be able to design normalized data models for OLTP systems and denormalized models for OLAP systems. Know how to identify data entities, attributes, relationships, and keys. Experience with modeling tools like ERwin is helpful.
Metadata Management
Understand the role of metadata in data systems. Know how to design metadata repositories and tag data assets throughout the data lifecycle. Experience with metadata catalogs like Azure Purview is desired.
Master Data Management
Implement proper master data management (MDM) techniques to ensure consistent definitions of core business entities. Know common MDM architecture styles like hub-and-spoke. Experience with MDM tools like Azure Data Catalog is a plus.
Data Pipeline Development
A data engineer needs to be able to develop and maintain the code for data pipelines. Key development skills include:
SQL and NoSQL
Have strong SQL skills for querying relational databases like Azure SQL Database. Know NoSQL query languages like Cosmos DB SQL API, Gremlin, and MongoDB API. Understand the difference between OLTP and OLAP workloads.
Python
Python is the most common ETL and data engineering programming language. Experience with Python libraries like Pandas, NumPy, and SQLAlchemy is highly desired. Know how to develop and debug Python code.
Scala/Spark
For big data pipelines, experience with the Scala language and Spark framework is very valuable. Understand resilient distributed datasets (RDD), and how to develop ETL jobs that scale.
Containerization
Understand how to package and deploy applications using technologies like Docker. Experience with dockerized data pipeline development is a major plus.
CI/CD
Implement CI/CD best practices for data engineering. Use source control, automated testing, and automated deployments. Experience with Azure DevOps is highly desired.
Data Visualization
Data engineers don’t just move and store data – they also need to make it understandable for analytics and reporting. Data visualization skills like:
Reporting
Know how to develop reports and dashboards that make data digestible for business users. Hands-on experience with BI tools like Power BI is highly desired.
Analytics/BI
Understand techniques like online analytical processing (OLAP) to analyze and extract insights from data. Experience with analytics and BI platforms like Azure Analysis Services is a plus.
Data Mapping
Be able to map raw datasets into visualizations like charts, graphs, and pivot tables. Know how to shape data into formats optimized for reporting and analytics.
Data Engineer Responsibilities
What does a data engineer actually do day-to-day? Typical responsibilities include:
- Designing and developing data storage architecture for structured, unstructured, batch, and streaming data
- Building scalable ETL and ELT data integration pipelines
- Developing and maintaining SQL and NoSQL code for data access and processing
- Creating data models and schemas optimized for analytics and reporting
- Implementing metadata management, master data management, and data governance processes and standards
- Monitoring data pipeline health, throughput, and performance
- Collaborating with data scientists, analysts, and engineers to ensure accurate business insights from data
- Identifying and remediating data quality issues and bottlenecks
Essential Soft Skills
In addition to the technical skills covered above, data engineers need certain soft skills, like:
Communication
Data engineers must communicate clearly with both technical and non-technical colleagues. You need to be able to explain data concepts and designs to business leaders.
Teamwork
Collaborating with other data professionals is crucial. Be able to work effectively in teams with data analysts, scientists, and BI experts.
Creativity
Data engineering requires creative problem-solving skills to build efficient pipelines and data models.
Business Acumen
Understand how data solutions add business value. Continually expand your knowledge of the company’s business goals and industry.
Agility
Be adaptive and agile to respond to changing data requirements and fail quickly. Thrive in modern data stacks that change rapidly.
Education Requirements
Most Azure data engineering roles require:
- Bachelor’s degree in computer science, information systems, engineering, mathematics, or a related quantitative field
- Master’s degree preferred but not always required
Ideal candidates also have:
- Relevant certifications like MCSE: Data Management and Analytics, Azure certifications, AWS certifications, or GCP certifications
- Hands-on experience through internships, co-ops, personal projects, open source contributions, or GitHub profile
Salary and Job Outlook
According to Glassdoor, the average base salary for an Azure data engineer in the United States is $125,291 per year. Total compensation with bonuses and equity can be $150k-$180k+. This salary reflects the high demand and competitive hiring market for Azure and data engineering skills.
The Bureau of Labor Statistics groups data engineers under the broader job category “database architect”. This occupation is projected to grow 11% over the next decade, much faster than the average for all occupations. Companies have an immense need for data professionals as they aim to extract insights from data using cloud platforms like Azure.
Getting Started
How can you start developing the key skills covered in this article? Here are some recommendations:
- Start learning Azure through online training on Microsoft Learn. Earn free certifications.
- Obtain hands-on experience through Azure free trials, student credits, or low-cost subscriptions.
- Learn a programming language like Python. Build projects for your portfolio.
- Practice your SQL and database skills. Design sample databases locally.
- Learn ETL techniques. Try running basic data pipelines.
- Build a strong computer science foundation through online courses.
- Earn a degree in a technical field like computer science or information systems.
With the right blend of cloud, data, programming, design, and communication skills, you can become a successful Azure data engineer.
Conclusion
Azure data engineers are in high demand as cloud data platforms like Azure continue maturing. Key skills for this role include:
- In-depth knowledge of Azure services and architecture
- Data engineering: ETL, modeling, warehousing, visualization
- Programming: Python, SQL, NoSQL, Scala/Spark
- Data management: Metadata, MDM, governance
- Development: CI/CD, testing, design patterns
- Soft skills: Communication, collaboration, creativity
- Relevant education and certifications
With the massive growth of data and migration to the cloud, there are abundant opportunities for data engineers to deliver value. By mastering Azure data engineering, you can propel your career to new heights.