Data engineering has become a critical role in today’s tech-driven world, especially for companies looking to harness the power of data for decision-making. If you’re aiming to advance your career as a Data Engineer, knowing exactly how to navigate the vast landscape of tools, technologies, and methodologies is vital.
I’m Bogdan, Lead Big Data Engineer at SoftServe with more than 10 years of experience in this field, working with various organizations — from innovative start-ups to major banking institutions. I focus primarily on Microsoft technologies. Along the way, I’ve also earned four Azure certifications (AZ-900, DP-900, DP-203, DP-600), so I’ve been around the block when it comes to building and managing data solutions in the cloud.
In this post, I’ll walk you through a comprehensive Data Engineering roadmap designed to help you build on your existing skills and stay ahead in this ever-evolving industry.
Learn more about the career path of a Big Data Engineer in our article 'What is Big Data engineering: career path to DataOps kingdom'.
WHAT IS A DATA ENGINEER, AND WHY DOES IT MATTER?
At the heart of every successful data-driven organization is the Data Engineer, the unsung hero who designs, builds, and maintains the architecture that supports vast amounts of data. Data Engineers work behind the scenes to ensure that data is organized, reliable, and accessible for data scientists, analysts, and decision-makers. Their role is crucial in industries ranging from finance and healthcare to e-commerce and tech.
Why does this matter? Because without skilled Data Engineers, organizations would struggle to manage the ever-growing amounts of data produced daily. In a world where data is gold, Data Engineers are the miners who bring that gold to the surface. Imagining an organization without Data Engineers is correlated with high decision times, unknown resources or consumptions, and a general state of unorganized information. We are the ones who create and maintain a good working environment in terms of data, with prospects of scalability, availability, and understandability. The safekeepers of data.
HOW TO FOLLOW THE DATA ENGINEER ROADMAP
You might feel that this discussion is relatively high-level, and you would be right because every organization comes with its own rules for data, its own purpose, structure, and usage. This is where the roadmap comes in, breaking it down step by step to see how you can enhance your skill set and excel in the world of data engineering, where no database is like another. Still, the core skillset required to understand them all is the same.
Step 1: Master the Fundamentals of Programming
Strong programming skills are essential for any Data Engineer. While Python and SQL dominate the landscape, knowing how to leverage these languages within modern environments is critical.
Python: As one of the most versatile languages in Data Engineering, Python offers powerful libraries like Pandas and NumPy for data manipulation and analysis. With its integration into platforms like Azure Databricks and cloud environments, Python is a must-have skill for building data pipelines and handling complex transformations.
SQL: SQL is critical for querying relational databases. As a Data Engineer, you'll work extensively with databases using SQL to structure, filter, and manage data. Whether you're interacting with Azure SQL Database, PostgreSQL, or MySQL, mastering SQL is a foundational skill.
Step 2: Understand Data Architecture
As a Data Engineer, building efficient and scalable systems is crucial for handling the growing amount of data in any organization.
Data Warehouses vs. Data Lakes: A Data Engineer often needs to manage both structured and unstructured data. Data warehouses, such as Azure Synapse Analytics or Snowflake, are optimized for running analytical queries, while data lakes (like Azure Data Lake or AWS S3) are designed to store raw, unprocessed data.
ETL/ELT Processes: One of the core responsibilities of a Data Engineer is managing data pipelines. Whether you’re using Azure Data Factory or open-source tools like Apache Airflow, understanding how to extract, transform, and load (ETL) data from various sources into usable formats is critical. These pipelines ensure that clean, structured data is available for analysis and decision-making.
Step 3: Leverage Cloud Computing
Cloud computing has revolutionized Data Engineering, providing flexibility to scale on demand and handle complex workflows.
Cloud Storage and Data Processing: Platforms like Azure, AWS, and Google Cloud allow Data Engineers to store, process, and analyze data at scale. Whether you're working with Azure Databricks for distributed processing or AWS Redshift, mastering cloud services ensures that your data infrastructure can grow with the business's demands.
Real-Time Data Processing: Many organizations now require real-time data processing in addition to batch processing. Tools like Azure Stream Analytics, Apache Kafka, and AWS Kinesis allow you to process data in real-time, providing immediate insights and responses to changing data.
Step 4: Orchestrate Data Workflows
Managing complex data workflows and automating processes is essential for efficiency.
Data Orchestration: Orchestration tools like Apache Airflow, Azure Logic Apps, or AWS Step Functions are crucial for scheduling and automating data pipelines. They allow you to handle complex data integration tasks and ensure that data moves smoothly between systems.
Monitoring and Automation: Robust monitoring is required to ensure that your data pipelines are running optimally. Tools like Azure Monitor, Datadog, or AWS CloudWatch give you visibility into system performance, while automation tools streamline routine tasks.
Step 5: Master Big Data Tools
As data grows in volume, velocity, and variety, mastering Big Data tools becomes essential for any Data Engineer.
Batch Processing with Apache Spark: Apache Spark is one of the most widely used frameworks for processing large-scale data. It allows for distributed computing, enabling you to run complex data transformations across multiple nodes. Whether you’re using Databricks (available on Azure, AWS, and GCP) or Apache Hadoop, batch processing skills are critical for managing big data workflows.
Real-Time Processing: As organizations seek real-time insights, tools like Apache Kafka, Flink, and Storm are becoming increasingly popular. These frameworks allow you to handle streaming data and apply real-time analytics, a very sought-after skill for modern data engineering. Implementing real-time pipelines ensures businesses can respond to data changes instantly, making them more agile.
Step 6: Data Governance and Security
Ensuring data quality, security, and compliance is a growing concern, especially with more stringent data regulations worldwide.
Data Governance: As a Data Engineer, you’ll need to implement data governance strategies that ensure data is accurate, consistent, and accessible to the right people. Tools like Azure Purview, AWS Glue Data Catalog, and Collibra help maintain data lineage, enforce security policies, and monitor data quality across pipelines.
Security and Compliance: With regulations like GDPR and CCPA, it’s crucial to ensure data is handled securely and that privacy concerns are addressed. Cloud platforms like Azure, AWS, and Google Cloud offer built-in security tools like Azure Security Center or AWS Identity and Access Management (IAM) to safeguard sensitive data and control access.
Step 7: Certifications and Continuous Learning
In the dynamic field of Data Engineering, staying ahead requires continuous learning and updating your skills. One of the most effective ways to showcase your expertise and commitment to growth is by earning certifications, especially within the Azure ecosystem, given its ever-growing presence in the cloud and data space.
Here are the most valuable Azure certifications for Data Engineers, along with a short overview of each:
- Microsoft Certified: Azure Fundamentals (AZ-900):
The AZ-900 certification is an excellent starting point for understanding the basic principles of Azure. It covers core concepts such as cloud computing models, Azure services, security, and compliance features. While not a technical exam, it lays the foundation for more advanced Azure certifications by providing a broad overview of cloud services. For those new to Azure or cloud environments, this certification helps solidify your understanding before diving deeper into the technical side. - Microsoft Certified: Azure Data Fundamentals (DP-900):
The DP-900 certification is specifically tailored for those starting in data-related roles on Azure. It focuses on the basics of core data concepts, such as relational and non-relational databases, data storage, and data processing. This certification is particularly useful for Data Engineers who want to build a strong foundational knowledge of Azure’s data services, such as Azure SQL Database, Azure Cosmos DB, and Azure Synapse Analytics. - Microsoft Certified: Azure Data Engineer Associate (DP-203):
As one of the most relevant certifications for Data Engineers, the DP-203 covers essential skills for designing and implementing data solutions on Azure. It dives into the technical aspects of data storage, processing, and security, focusing on key Azure services like Azure Data Lake, Azure Data Factory, Azure Databricks, and Azure Synapse Analytics. The DP-203 certification ensures that you can manage end-to-end data solutions, including data ingestion, transformation, and storage, making you a highly valuable asset in the Azure ecosystem. - Microsoft Certified: Azure AI Engineer Associate (AI-102) (Optional for Data Engineers):
While not strictly a Data Engineering certification, AI-102 can complement your skills if you’re working on AI-driven data projects. It focuses on Azure's AI services, including Azure Cognitive Services and Azure Machine Learning. For Data Engineers looking to expand into AI and machine learning, this certification provides an excellent pathway to understand how data and AI intersect in the Azure cloud.
WHY AZURE CERTIFICATIONS MATTER FOR DATA ENGINEERS
These Azure certifications not only validate your knowledge but also position you as a leader in cloud-based data solutions. They help you:
- Stay Current: Azure’s cloud services are constantly evolving, and earning certifications keeps you up-to-date with the latest features and best practices.
- Advance Your Career: Having recognized Azure certifications on your resume gives you a competitive edge, whether you’re looking to move up within your company or explore new opportunities in the Data Engineering field.
- Improve Job Performance: Certifications like DP-203 equip you with the hands-on skills necessary to implement and manage data pipelines efficiently. You’ll be better equipped to optimize data flows, ensure data security, and deliver robust data solutions.
- Network with Azure Experts: Certification exams and learning paths often introduce you to broader Azure communities, offering networking opportunities with other cloud professionals who can share insights and tips on working with Azure’s Data Engineering tools.
Continuous learning is encouraged through the requirement of a recap of the major elements of a certification after a certain period. Right now, for a more advanced certification, like DP-203, a short retest every 12 months is required in order to prolong the expiration date. This increases the prestige of the certification and ensures a good and permanent qualification of the bearers.
By focusing on earning and maintaining your Azure certifications, you ensure that your skill set is both current and relevant, empowering you to handle complex data solutions in the cloud with confidence.
CONCLUSION
Mastering the Data Engineer roadmap requires dedication, a strategic approach, and continuous learning. By focusing on programming, data architecture, cloud computing, big data tools, and security, you can position yourself to thrive in this fast-growing field. The combination of technical expertise and the ability to adapt to new technologies will ensure that you remain a valuable asset in any organization.
Author: Bogdan Balcan, Lead Big Data Software Engineer at SoftServe Romania.
