Microsoft is hiring an early-career Software Engineer for its Azure Data organization in Bangalore. The role sits within the Data Engineering One Infra team, which builds and maintains the infrastructure behind Microsoft Fabric Spark, Azure Synapse Spark and Cosmos Analytics. These services run millions of Spark jobs every day, so the work you support affects how reliably customers process and analyze data. You will write maintainable code, help debug production issues, take part in code reviews and learn how large-scale distributed systems are operated, with guidance from experienced engineers. The position follows a hybrid model with one day per week in the office.
Most entry-level engineering roles keep you close to a single feature. This one places you inside the infrastructure layer that keeps one of Microsoft’s biggest analytics platforms running, which is a very different and often faster way to learn how cloud software behaves at scale.
Where This Team Fits
The Azure Data Analytics team builds the core experiences inside Microsoft Fabric, including the Data Warehouse, Data Engineering, Data Science, AI and Power BI workloads. The Data Engineering One Infra group is the layer underneath, providing the compute and infrastructure that Spark-based workloads depend on. You will not be building dashboards. You will be working on the systems that make those dashboards, notebooks and pipelines possible.
Hybrid Full-Time Early Career
A Typical Day In This Role
Expect your time to be split between writing code, reading logs and telemetry, and working through reviews. As a newer engineer you will mostly write extensible, maintainable code that follows the team’s established patterns. You will also assist with debugging and testing, and learn to spot problems early through logs, telemetry and automated code analysis. Everything happens under oversight, so questions are expected and nobody assumes you already know the codebase.
Responsibilities You Will Own
Area | What It Looks Like In Practice |
|---|---|
Coding | Writing clean, maintainable code using agreed patterns |
Debugging | Investigating issues with logs, telemetry and code analysis tools |
Design Support | Helping test design options and understanding dependencies, performance, scalability and security |
Code Reviews | Learning team standards and gradually reviewing peers’ work |
Security | Supporting code invariants, security testing, monitoring and compliance work |
Collaboration | Working with partner teams on integration, end-to-end testing and deployments |
On-Call | Helping resolve service degradation, downtime or interruptions |
Working With AI Tools Responsibly
Microsoft expects engineers to use AI tools across the development lifecycle, and you will also learn to review AI-generated code changes critically. This matters more than it sounds. Reviewing generated code well requires you to understand what the code should do, which is a useful habit to build early in your career.
Candidates who can explain why a change is correct, and not only that it compiles, tend to stand out in engineering interviews.
On-Call And Production Reality
The role includes on-call support for customer-facing issues. Be realistic about this before you apply. On-call means being reachable when a service degrades, and it is also where much of the real learning happens. You will see how incidents are triaged, how issues are escalated, and how teams communicate under pressure. You work with direct guidance and are encouraged to escalate when needed.
Eligibility At A Glance
Requirement Type | Details |
|---|---|
Minimum | Associate’s Degree or Apprenticeship in Computer Science or a related field, plus 6+ months of work or internship experience coding in C, C++, C#, Java, JavaScript or Python, or equivalent experience |
Preferred | Bachelor’s Degree in Computer Science or a related technical discipline with proven coding experience, or equivalent experience |
Screening | Must pass the Microsoft Cloud Background Check on hire and every two years afterward |
Background Check Required
Skills Worth Building Before Applying
You do not need to be a Spark expert, but familiarity helps. Spend time on Apache Spark fundamentals such as partitions, shuffles and how jobs are scheduled across a cluster. Strengthen your command of one language from the list, ideally C#, Java or Python, and get comfortable reading other people’s code. Basic knowledge of distributed systems, such as fault tolerance, retries and scaling, will help you follow team discussions. Short projects that process a real dataset with Spark, or that show how you debugged a failing job, make better resume material than long tool lists.
Spark Python C# Java Azure Git
What Recruiters May Evaluate
Interviews for roles like this usually test coding fundamentals first: data structures, algorithms and clear problem solving. Expect questions on how you debug an unfamiliar issue, how you reason about performance, and how you work with others. Be ready to walk through an internship or project in detail, covering what you built, what broke and what you changed. Clear communication counts for a lot, because on-call and cross-team work both depend on it.
flowchart LR A[Online Application] --> B[Resume Screening] B --> C[Coding Interviews] C --> D[Team Discussion] D --> E[Offer]
Long-Term Career Direction
Early exposure to big data infrastructure and distributed systems is a strong base for later work in platform engineering, data engineering or cloud reliability. Working on a product like Fabric also builds familiarity with the wider Azure data ecosystem, which is a valuable skill set as more companies move analytics to the cloud.
Keywords For Resume
Software Engineer • Apache Spark • Microsoft Fabric • Azure Synapse • Distributed Systems • Big Data • C# • Java • Python • Debugging • Telemetry • Code Reviews • Cloud Infrastructure • Azure Data • On-Call Support • Data Engineering
Final Thoughts
This is a practical entry point for freshers and early-career engineers who want to learn how large analytics platforms are built and kept reliable. The scale is large, the learning curve is steep, and the on-call component is real. Candidates who enjoy debugging and want to understand systems beyond the application layer will likely find it rewarding.
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