Description
As a Software Dev Engineer III at Swiggy, 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.
Responsibilities:
- 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.
Requirements:
- 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.
Nice to 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 documented, observable, and safe to evolve.