Responsibilities
- Design and build real-time and batch data processing systems
- Develop event-driven data pipelines using streaming technologies
- Build scalable ingestion, transformation, enrichment, and delivery services
- Design data platform components that support millions of events and large-scale analytical workloads
- Improve reliability, observability, monitoring, and operational excellence across data systems
- Participate in architecture reviews and technical design discussions
- Own production systems, incident response, and operational improvements
- Work closely with Product, Infrastructure, Analytics, and Engineering teams
- Mentor engineers and contribute to engineering standards and best practices
Requirements
- 6+ years of experience in Software Engineering, Data Engineering, Platform Engineering, or Backend Engineering
- Strong programming experience in Java, Go, Python, Rust, Scala, or similar languages
- Experience designing and operating distributed systems in production
- Experience building and supporting streaming or real-time data platforms
- Experience with Kafka, Flink, Spark Streaming, Kinesis, Event Hubs, or similar technologies
- Experience operating cloud-native systems on AWS, Azure, or GCP
- Experience troubleshooting production issues and supporting critical systems
Nice to Have
- Experience designing event-driven architectures
- Experience with CDC, event sourcing, or asynchronous processing patterns
- Experience building low-latency data systems
- Experience with infrastructure-as-code (Terraform, CloudFormation, Pulumi)
- Experience implementing CI/CD pipelines and deployment automation
- Experience with observability, monitoring, alerting, and reliability engineering
- Experience designing platform-level frameworks used by multiple teams
- Experience with data lakehouse platforms, Snowflake, Databricks, or large-scale analytical systems
Work Arrangement
Remote (Worldwide)
