About the role
Responsibilities:
- Design, develop, and maintain scalable backend and data-platform systems from the ground up.
- Build highly reliable data-intensive services, APIs, pipelines, and platform components.
- Design and develop event-driven architectures using Kafka for real-time data processing and distributed systems.
- Work with large-scale datasets and build systems for data ingestion, processing, storage, and serving.
- Own backend and data-platform architecture, technical design, development, testing, deployment, and production reliability.
- Make key decisions around system architecture, data architecture, technology selection, scalability, performance, and reliability.
- Develop distributed systems that support AI/ML and generative AI use cases.
- Work closely with founders and product teams to translate business requirements into scalable backend and data-platform solutions.
- Establish engineering best practices around code quality, testing, observability, CI/CD, and production operations.
- Take end-to-end ownership of projects from architecture and development through production.
- Contribute to defining the engineering standards and foundational data infrastructure of the organization.
Requirements:
- 3-7 years of backend/software engineering experience.
- Strong experience in Scala or another JVM/backend language.
- Hands-on experience with Apache Kafka and event-driven architectures.
- Strong SQL and database knowledge.
- Experience building data platforms, data pipelines, or data-intensive backend systems.
- Experience with distributed systems/microservices.
- Strong understanding of APIs and backend services.
- Experience with AWS/GCP/Azure.
- Good understanding of system design, scalability, reliability, and performance.
- Good-to-have: Python / Java / Go / Node.js, Spark / Flink, Databricks, Airflow, data lake/lakehouse architecture, streaming data pipelines, AI/ML or GenAI platform exposure, observability, and production infrastructure.