Senior Python / Cloud Engineer

Sigma Lab is a US-based stealth startup operating at the intersection of cybersecurity, artificial intelligence and large-scale data processing. They build an advanced platform designed for complex, mission-critical environments where thousands of devices and systems continuously generate massive volumes of operational and sensor data. The platform combines AI, cloud infrastructure and large-scale data processing to identify security threats, anomalies, and potentially dangerous events across distributed physical and digital environments. The technology supports environments with 24/7 operation, continuous telemetry generation, and critical requirements for uptime, security incident detection, and large-scale data processing. The company currently operates in stealth mode, sharing detailed product and architecture information during recruitment.

• 5+ years of professional software engineering experience
• Strong commercial experience with Python
• Strong hands-on experience with Google Cloud Platform
• Experience designing and running production-grade cloud systems
• Experience with Kubernetes, preferably Google Kubernetes Engine (GKE)
• Experience with BigQuery
• Experience with Apache Spark or similar distributed data processing technologies
• Strong knowledge of SQL
• Experience with Docker
• Experience processing large volumes of data
• Good understanding of distributed systems, scalability, fault tolerance, and high availability
• Experience troubleshooting and optimizing production cloud environments
• Ability to make independent technical and architectural decisions
• Good command of English

Nice to have:

• Dataflow / Apache Beam
• Dataproc
• Pub/Sub or Kafka
• Cloud Composer / Airflow
• Cloud Run
• Terraform / Infrastructure as Code
• Streaming and event-driven architectures
• Data lakes and large-scale analytics platforms
• Cloud security, IAM, and service accounts
• Monitoring and observability of distributed systems
• GCP and BigQuery cost optimization
• Experience with cybersecurity, OT/IoT, industrial systems, or critical infrastructure

• Designing and developing scalable backend and data processing systems in Python
• Building cloud-native solutions on Google Cloud Platform
• Designing and maintaining production workloads running on Kubernetes / GKE
• Building and optimizing data pipelines processing high volumes of telemetry and event data
• Working with BigQuery for large-scale storage, querying, and analytics
• Developing distributed data processing workloads using Apache Spark
• Working with services such as Cloud Storage, Pub/Sub, Dataflow, and Dataproc
• Designing architectures for scalability, reliability, and high availability
• Building systems capable of handling continuously growing data volumes
• Optimizing cloud infrastructure for performance and cost
• Monitoring, debugging, and improving distributed production systems
• Supporting data infrastructure used by AI-driven analytics
• Contributing to architectural decisions and technology selection
• Designing systems resilient to partial failures and infrastructure disruptions
• Improving performance, observability, and operational reliability