Azure Data Factory Engineer
How to become an Azure Data Factory engineer - Career Guide
- In today’s world, businesses rely heavily on cloud platforms to manage and move data securely and efficiently.
- One of the most powerful tools for data movement and transformation is Azure Data Factory (ADF), a fully managed cloud-based data integration service provided by Microsoft.
- As companies generate more data than ever before, the demand for professionals who can manage, automate, and optimize this data flow is rising rapidly.
- Learn how to become an Azure Data Factory engineer with step by step guidance, skills required, top training options, and career insights.
- Enter the Azure Data Factory Engineer – a key player in building modern data pipelines and transforming raw data into actionable insights.
- Whether you’re from a non-IT background or just starting your tech journey, this guide will help you step-by-step in becoming an Azure Data Factory Engineer.
- We’ll cover the required skills, tools, career roadmap, certifications, and even recommend the top training institutes in Hyderabad to help you get started.
What is Azure Data Factory?
Azure Data Factory (ADF) is a cloud-based ETL (Extract, Transform, Load) service that helps move and transform data from various sources into centralized storage for analytics and reporting. It supports integration between databases, APIs, files, cloud systems, and more. Think of it as a factory that moves data through pipelines to turn raw data into meaningful reports.
Key Features:
- No-code and low-code interface
- Scheduling and monitoring of data workflows
- Integration with Azure Synapse, SQL, Blob Storage, etc.
- Trigger-based automation
Why Become an Azure Data Factory Engineer ?
- High Demand: Companies globally are adopting Azure for their data needs.
- Lucrative Salaries: Average salary in India ranges between ₹7–10 LPA.
- Fast-Growing Career: Roles in data engineering are expanding rapidly.
- Global Opportunities: Skills are relevant across industries and countries.
Future-Proof Role: Cloud data integration is the backbone of modern analytics.
Skills Required to Become an Azure Data Factory Engineer
1. Technical Skills (Elaborated)
Basics of Cloud Computing (Azure preferred)
Understanding cloud computing is crucial. Learn about:
- What is cloud storage and cloud services
- Types of cloud models: IaaS, PaaS, SaaS
- Azure’s architecture and services
- Benefits of cloud vs traditional servers
Start with Microsoft Azure Fundamentals (AZ-900) to build a solid base.
SQL and Data Querying Skills
SQL is used to:
- Extract and manipulate data
- Write queries to clean and filter data
- Join multiple data tables
- Perform aggregations and summaries
You’ll use SQL in ADF to query source systems or during transformations in Data Flows.
Data Warehousing Concepts
You need to understand how large amounts of data are stored and accessed.
- Learn about OLAP vs OLTP
- Understand fact and dimension tables
- Familiarize yourself with Star Schema and Snowflake Schema
- Learn how data warehousing supports BI tools
These concepts help in building effective data pipelines that feed analytics platforms.
ETL Tools
As an ADF engineer, you should understand ETL:
- Extract: Pulling data from sources like Excel, APIs, SQL Servers
- Transform: Cleaning, reshaping, and enriching data
- Load: Moving cleaned data into databases, data lakes, or warehouses
Get hands-on with:
- Azure Data Factory (ADF)
- SSIS (SQL Server Integration Services)
- Talend (optional for variety)
Azure Services
ADF does not work alone. Know how to integrate with:
- Azure Blob Storage – to store raw files like CSV, Excel, JSON
- Azure SQL Database – to load or transform structured data
- Azure Synapse Analytics – for big data analysis and reporting
Understanding these services improves your ADF architecture design.
Python or Data Scripting Basics
ADF supports running custom logic using:
- Azure Batch or Azure Functions
- Databricks notebooks (Python, Spark)
- Stored procedures in SQL
You don’t have to be a pro coder, but basic Python scripting, data types, and functions are useful.
CI/CD with Azure DevOps (Optional but Valuable)
Modern data engineering pipelines use CI/CD (Continuous Integration and Delivery):
- Learn Git version control
- Use Azure DevOps pipelines to automate ADF deployments
- Understand JSON ARM templates used by ADF
This skill is great for enterprise-level roles.
2. Soft Skills (as before)
- Problem-solving mindset – Break down data issues logically
- Logical thinking – Map data flows and relationships
- Attention to detail – Ensure pipeline accuracy
- Communication skills – Work with analysts, document solutions clearly
Step by Step Career Path (Detailed)
Step 1: Understand Cloud and Data Basics
Why this step matters:
You must first understand where and how data is stored, moved, and processed in the digital world.
What to learn:
- What is cloud computing?
Meaning of Cloud computing is to store and access the data on the internet instead of your computer. Microsoft Azure is one of the biggest cloud providers. - What is a database?
A place where data is kept neatly and easy to find. For example, customer names and their orders. - Difference between cloud and traditional storage
Cloud is faster, safer, and always available — making it ideal for business.
How to start:
- Watch free YouTube videos about cloud basics
- Explore beginner courses on Microsoft Learn
- Read blogs on Azure fundamentals
Step 2: Learn the Fundamentals of Data Engineering
Why this step matters:
Data engineering is the base for learning ADF. It teaches you how raw data becomes useful.
What to learn:
- What is data engineering?
Data collection, cleaning the data, and storing it so analysts can use it. - Structured vs Unstructured data:
Structured data = tables (like Excel)
Unstructured data = images, videos, PDFs - Batch vs Streaming data:
Batch = processed in groups (e.g., daily sales)
Streaming = real-time (e.g., live stock prices) - Data Lakes and Data Warehouses
Data Lake = raw storage
Data Warehouse = cleaned, ready-to-use data
How to start:
- Read beginner guides on data engineering
- Take a short course on Coursera or EdX
- Understand basic data flow diagrams
Step 3: Get Familiar with Azure
Why this step matters:
Azure is the backbone cloud service behind Azure Data Factory. Knowing its tools makes learning ADF easier.
What to learn:
- Azure Portal navigation – understand menus, dashboards, and services
- Azure Storage – learn where data is stored
- Azure SQL Database – cloud version of a regular database
- Azure Monitor – helps track usage and performance
How to start:
- Create a free Azure account
- Use sandbox labs on Microsoft Learn
- Practice uploading and downloading files in Azure Blob
Step 4: Dive Deep into Azure Data Factory
Why this step matters:
This is your core skill. Everything you learn from now builds your job-specific expertise.
What to learn:
- Pipelines:
A set of steps that move or change data. Like a recipe with multiple steps. - Datasets:
Represent your data source or destination. Example: Excel file, SQL table. - Linked Services:
Connection info for your data like username/password to access storage. - Activities:
Individual tasks like copying, transforming, or looking up data. - Triggers:
Schedule when your pipeline should run (e.g., daily at 9 AM)
How to start:
- Enroll in a beginner ADF course
- Use Azure Portal to create demo pipelines
- Build a basic pipeline to transfer data from an Excel sheet to Azure SQL Database.
Step 5: Build Real-Time Projects
Why this step matters:
Companies want candidates who’ve done it, not just studied it.
Project ideas:
- Move sales data from CSV files to Azure SQL using ADF
- Create a pipeline to clean messy data (e.g., removing blanks or duplicates)
- Schedule a pipeline to run daily and send an email notification
Where to get data:
- Kaggle.com (free datasets)
- Government open data portals
- Your own dummy data (made in Excel)
Step 6: Get Certified
Why this step matters:
Certifications show that you’re serious and capable they also help you stand out.
Recommended certifications:
- AZ-900: Microsoft Azure Fundamentals
Great for complete beginners. - DP-900: Data Fundamentals
Covers core concepts of data, storage, and processing. - DP-203: Azure Data Engineer Associate
Industry-standard certificate for ADF and data engineering roles.
How to prepare:
- Microsoft Learn + YouTube videos
- Free practice tests online
- Paid courses from top institutes (see below)
Expand Your Skills with Job-Ready Courses
If you’re preparing for certifications and real-world projects, consider these career-focused programs by top institutes in Hyderabad:
- Courses by AzureTrainings:
Visit AzureTrainings : https://azuretrainings.in
2. Courses by Brolly Academy:
- Azure Data Factory Course
- Azure DevOps Course
- Data Engineering Certification Program
Check out Brolly Academy courses : https://brollyacademy.com
These programs combine theory + real-time projects and are great for beginners and professionals alike.
Step 7: Create Your Portfolio
Why this step matters:
Portfolios prove your ability to potential employers.
What to include:
- GitHub repositories with ADF projects
- Project documentation (explain what you did)
- Resume with your tools, skills, and certifications
- LinkedIn profile with posts about your learning journey
Tip:
Write short blogs on Medium or LinkedIn about your learning progress and ADF tips.
Step 8: Apply for Jobs
Why this step matters:
Once you’re job-ready, you must apply smartly to land interviews.
Where to apply:
- LinkedIn: Search for “Azure Data Factory Engineer”, “ADF Developer”, “Data Engineer”
- Job Portals: Naukri, Indeed, Monster
- Company websites: Apply to companies hiring in cloud/data tech
- Referrals: Join LinkedIn groups, Hyderabad tech meetups
Azure Data Factory Engineer vs Azure Data Engineer
Feature | Azure Data Factory Engineer | Azure Data Engineer |
Definition | Works mainly with Azure Data Factory to build ETL pipelines | Handles end-to-end data architecture on Azure cloud |
Primary Focus | Data movement, transformation, and orchestration | Data modeling, storage, integration, and optimization |
Key Tools Used | Azure Data Factory, Azure SQL, Blob Storage | ADF, Azure Synapse, Azure Data Lake, Databricks, Azure DevOps |
Skill Requirement | Low-code/no-code, SQL, basic data flow understanding | SQL, Python, Spark, CI/CD, data lake concepts, Azure architecture |
Real-world Example | Creating a pipeline to move data from Excel to Azure SQL DB | Designing a full pipeline to ingest real-time data from IoT sensors |
Certifications | DP-900, AZ-900 (entry-level) | DP-203 (Azure Data Engineer Associate) |
Typical Job Role | ADF Developer, ETL Developer (Azure-based) | Azure Data Engineer, Big Data Developer |
Career Scope | Niche, but high demand for ADF-focused roles | Broad scope; opens doors to ML, analytics, and architecture roles |
Best For | Beginners, those transitioning into data careers | Intermediate professionals aiming to scale Azure data infrastructure |
Hyderabad’s Best Azure Data Factory Coaching Institutes
1. AzureTrainings
- Offers specialized ADF + Azure Data Engineer courses
- Real-time projects & mock interviews
- Weekend and weekday batches available
2. Brolly Academy
- Known for quality training and certification support
- Project-based learning and resume building
3. Kelly Technologies
- Focuses on ADF along with Synapse and Power BI
- Lab sessions and certification prep included
4. Mind Q Systems
- Ideal for freshers and job seekers
- Interview-focused training
5. RS Trainings
- Instructors with 10+ years of experience
- Offers placement assistance
Azure Data Factory FAQs
Yes! Start with basics and move step by step. Many successful professionals came from non-tech backgrounds.
Not necessarily. Most workflows are no-code or low-code. Knowing some SQL and Python helps but isn’t required at the start.
With dedication, 2 to 3 months is enough to become job-ready if you follow a structured path.
Not mandatory, but they add great value and improve your chances during interviews.
Azure Data Factory is mainly used for data movement and transformation (ETL/ELT), while Azure Synapse is a big data analytics service that combines data warehousing and big data capabilities
Yes, but it’s recommended to first understand the basics of Azure – especially Blob Storage, Azure SQL, and the Azure Portal navigation
- Copy data from on-prem SQL to cloud storage
- Transform CSV files and load into Azure SQL
- Automate workflows using triggers (daily/weekly jobs)
- Create error-handling logic with conditional activities
- Microsoft
- Infosys
- Accenture
- TCS
- Deloitte
- Startups and mid-size analytics firms
- Any data-focused enterprise using Azure ecosystem
With most organizations migrating to the cloud, ADF Engineers are in huge demand. The job market is strong in cities like Hyderabad, Bangalore, Pune, Chennai, and Mumbai.
AzureTrainings is widely regarded as one of the best institutes to learn Azure Data Factory in Hyderabad. It offers hands-on training, real-time projects, and industry-based curriculum tailored for data engineering careers. Both beginners and working professionals benefit from their structured courses and placement assistance.
Yes, Brolly Academy is known for providing high-quality Azure Data Engineering training in Hyderabad. Their courses include Azure Data Factory, Azure Synapse, and other essential services. Brolly’s expert-led sessions and real-time project support make it ideal for job-oriented learning
For job-ready Azure Data Factory training in Hyderabad, AzureTrainings and Brolly Academy are two top choices. They offer practical labs, resume preparation, interview guidance, and placement support, making them ideal for aspiring data engineers.
You don’t need prior cloud experience. However, basic understanding of databases, data types, and simple SQL queries can make learning Azure Data Factory smoother.
Not at all. While large enterprises do use it extensively, ADF is also suitable for small- and mid-sized businesses that want to automate their data flow and reporting processes in the cloud.
Azure Data Factory mainly handles batch processing, but when combined with services like Azure Stream Analytics or Event Grid, it can also handle near-real-time scenarios.
While both are ETL tools, ADF is cloud-native, scalable, and integrates better with Azure services. SSIS is on-premise, often used within SQL Server environments.
Yes! Azure Data Factory supports integration with SAP, Salesforce, Amazon S3, Google BigQuery, and many other platforms using linked services and custom connectors.
Yes, ADF provides built-in monitoring dashboards, alerts, and integrates with Azure Monitor and Log Analytics for automation and alerts.
ADF is part of Azure’s managed services, which means Microsoft continuously updates it with new connectors, features, and enhancements—often without requiring user intervention
Yes, using Azure Batch, Databricks notebooks, or Azure Functions, you can run custom Python or R scripts as part of your ADF pipelines.
Conclusion
Becoming an Azure Data Factory Engineer is a promising and achievable goal – even for non-IT individuals. With the right learning path, tools, and mentorship, you can build a rewarding career in cloud data engineering. Use this guide as your map, start learning today, and grow your skills one step at a time.
Don’t forget to explore more about Data Engineering to strengthen your foundational understanding.
Let Azure Data Factory be your gateway into the cloud data world. You’ve got this!