Brinqa - Principal Software Engineer – Data Team
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Requirements
• 7+ years of professional software development experience. • Proven experience building enterprise SaaS systems. • Demonstrated ability to operate autonomously and define technical direction with limited guidance. • Experience working across the full stack or deep expertise in backend and data systems architecture. • Professional proficiency in at least one of the following languages: Java, Python, or Typescript. • Experience deploying and operating systems in GCP. • Strong written and verbal communication skills with the ability to justify architectural decisions clearly. • Proficiency in Java, Python, and Typescript. • Experience designing ingestion pipelines, enrichment engines, or large-scale analytics systems. • Experience with distributed databases (e.g., Yugabyte, Postgres) or similar technologies. • Multi-cloud experience (AWS and/or Azure). • Experience with streaming architectures or high-throughput event processing systems. • Demonstrated effective use of AI coding tools in professional software development workflows. • Experience mentoring engineers at senior levels. • MS in Computer Science, Computer Engineering, Electrical Engineering, or related discipline. • Competency Expectations • Demonstrates in-depth to expert knowledge in one or more technical domains • Cross-team technical influence. • Repeated success delivering complex enterprise systems. • Ability to lead technical direction without formal managerial authority. • Ownership of architectural quality, scalability, and long-term maintainability. • Leadership through mentorship and technical example. • Impact & Growth • Impact & Growth • Shape the evolution of Brinqa’s data plane architecture. • Influence enterprise-scale cyber risk analytics systems. • Help define multi-cloud data architecture strategy. • Drive AI-augmented engineering practices within a high-performing team.
Responsibilities
• Architecture & Technical Leadership • Design and implement scalable, performant data plane components supporting high-volume ingestion and analytics workloads. • Contribute to the evolution of the data plane architecture as system requirements expand. • Evaluate and justify design trade-offs across distributed systems, data storage, and compute layers. • Influence technical decisions that impact multiple teams and platform components. • Ensure solutions meet enterprise-grade performance, reliability, and scalability expectations. • Data Systems Engineering • Develop systems that handle large-scale ingestion, transformation, enrichment, and persistence. • Support both real-time and batch data processing workflows. • Design for multi-cloud extensibility (GCP primary; AWS and Azure initiatives emerging). • Contribute to distributed database and storage strategy. • AI-First Development Practice • Use AI-assisted development tools (e.g., Antigravity, ClaudeCode, and similar) in a disciplined, multiplicative way. • Increase engineering throughput and code quality through AI-augmented workflows. • Establish best practices for responsible and effective AI tool usage within the team. • (Optional) Contribute ML/AI system knowledge where applicable. • Collaboration & Influence • Partner with executive leadership and field engineering to refine technical requirements. • Participate in high-level solution discussions impacting enterprise customers. • Mentor engineers of similar or junior level, especially in areas of specialized domain knowledge. • Contribute to documentation, standards, and internal technical guidance.
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