Software Engineering

Microsoft Microsoft · Big Tech · Bengaluru, KA, IN · Software Engineering

Software Engineer role focused on building and maintaining a Fabric-based SaaS platform, including HDInsight components. Responsibilities involve writing code, debugging distributed systems, working with big data technologies like Spark, and utilizing AI tools in the daily workflow. The role emphasizes growth in distributed systems, cloud platform engineering, and large-scale data infrastructure.

What you'd actually do

  1. Write clean, maintainable code in C#, Java, Scala, or Python for Fabric Materialized Lake View services and HDInsight components. Use AI tools and coding best practices across the development lifecycle.
  2. Explore design options for data refresh, scheduling, and query optimisation features with guidance from senior engineers. Contribute to design documents and technical specifications.
  3. Review code from teammates to check for correctness, test coverage, and adherence to team standards. Participate in code reviews to learn coding patterns and build familiarity with the Spark and Fabric codebase.
  4. Debug issues in distributed systems running on Azure, Linux, and Windows. Use debugging tools, logs, and telemetry to investigate problems. Learn to conduct incident retrospectives and implement fixes with supervision.
  5. Support live site operations on a rotational, on-call basis. Follow playbooks to diagnose and mitigate issues in Fabric and HDInsight services; escalate complex problems to senior engineers.

Skills

Required

  • Bachelor’s degree in computer science or related technical discipline
  • proven experience coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python

Nice to have

  • Master’s degree in computer science or related technical field
  • proven coding experience
  • Experience with the Azure stack including Storage, Compute, Networking, Fabric, Purview, Synapse, AKS, DevOps, Data Factory, or Power BI.
  • Familiarity with big data technologies such as Spark, Kafka, Hadoop, or HBase.
  • Exposure to data lakes, data engineering tools, or container-based architectures (Docker, Kubernetes)