Software Dev Engineer III

About the role Swiggy’s Data Platform enables teams to move, process, and use data reliably at scale. As an SDE III, you will design and build foundational platform capabilities across data ingestion, distributed compute, storage, and serving. You will own problems end to end—from architecture and implementation through rollout, observability, and operations—and help raise the engineering bar for the team. This role is for an engineer who enjoys solving difficult systems problems: keeping data pipelines dependable as volumes and workloads grow, making platform behavior predictable for users, and improving performance and cost without compromising correctness or governance. What you’ll do Design, build, and operate scalable data-platform services and distributed systems used by product, analytics, and machine-learning teams. Develop reliable batch and streaming data processing and ingestion capabilities, with clear guarantees around correctness, recovery, and data freshness. Improve the performance, availability, and cost efficiency of data workloads through sound architecture, measurement, and iterative optimization. Build platform abstractions and self-service workflows that make common data tasks easier and safer for engineering and data users. Establish strong operational practices: observability, alerting, capacity planning, incident response, and durable root-cause fixes. Apply access control, data governance, and safe change-management practices to production systems and datasets. Lead technical design reviews, break down ambiguous problems, and make pragmatic decisions that balance speed, reliability, and maintainability. Partner with infrastructure, product, analytics, and machine-learning teams to understand needs and deliver platform capabilities with measurable outcomes. Mentor engineers through design and code reviews, and contribute to a culture of ownership and continuous improvement. What you’ll bring 8+ years of software engineering experience building and operating production systems, including meaningful experience with distributed systems or data infrastructure. Strong programming skills in one or more languages such as Java, Scala, Python, or Go, and the ability to write maintainable, well-tested code. A solid understanding of distributed computing concepts, data structures, concurrency, fault tolerance, and system design. Hands-on experience with large-scale data processing or streaming technologies—for example, Apache Spark, Kafka, Flink, or comparable systems. Experience designing reliable data pipelines and working with data storage formats, lakehouse systems, or analytical warehouses. Good SQL skills and an understanding of data modeling, partitioning, performance tuning, and data-quality concerns. Experience operating cloud-based production systems, including monitoring, troubleshooting, and managing service reliability. A strong sense of ownership: you can take a problem from an unclear requirement to a production solution and its ongoing health. Clear communication skills and the ability to work effectively across engineering and data-focused teams. It’s a plus if you have Experience with platforms such as Databricks, Delta Lake, Apache Iceberg, Snowflake, or similar technologies. Experience building multi-tenant platforms, developer tooling, or self-service infrastructure. Experience with data governance, lineage, access control, or privacy-aware data systems. A track record of improving reliability, performance, or infrastructure cost at significant scale. Experience leading technical initiatives and helping other engineers grow. How success looks Data workloads become more reliable and easier to operate as platform usage and complexity grow. Platform users can onboard and run workloads with less manual intervention and clearer operational feedback. Improvements to performance, cost, freshness, or reliability are measurable and sustained. Technical decisions and production changes are well documen