Staff Software Engineer

Roles & Responsibility:

• Design and own end-to-end architectures across microservices, event-driven systems, and data platforms.
• Build and deliver high-quality backend services and APIs that are reliable, secure, and scalable.
• Design and operate cloud-native infrastructure on Kubernetes, applying platform engineering best practices.
• Build and maintain data pipelines and products on Spark and Databricks where required.
• Drive event-driven architecture using Kafka and streaming technologies.
• Write clean, testable, and maintainable software primarily in Scala with Python where appropriate.
• Lead architecture and design reviews; author lightweight design documents for major decisions.
• Define and improve engineering standards across testing, observability, CI/CD, and reliability.
• Coach and mentor engineers at multiple levels through reviews, design sessions, and pairing.
• Build observability into systems from day one (logs, metrics, tracing, alerting, SLOs).
• Deliver through CI/CD with safe release, rollback, and progressive delivery strategies.

Skills

• 8+ years of professional software engineering experience across backend, platform and or data domains.
• Strong programming skills in Scala or Java (Scala preferred); other languages are a plus.
• Proven experience building distributed microservices and event-driven systems ideally in the Scala / JVM ecosystem (we use KStreams, Flink, cats-effect, fs2 & tapir)
• Hands-on experience with Kafka in production at scale.
• Solid experience designing and developing APIs (REST and/or event-driven interfaces).
• Strong knowledge of relational databases (e.g. PostgreSQL), data modeling, and query optimization.
• Experience building distributed data pipelines with Spark and/or Databricks.
• Demonstrated ability to lead technical design and architecture decisions across teams.
• Experience mentoring and growing engineers.

Good To Have:

• Working knowledge of Kubernetes — workloads, networking, Helm, scaling, and operational practices.
• Experience with data governance, lakehouse table formats (Delta, Iceberg), and modern data platforms.
• Experience with platform engineering — building developer platforms, golden paths, or internal tooling.
• Performance profiling and JVM tuning experience.
• Experience with MLOps or data science platform integrations.