Senior Data Engineer

As a Senior Data Engineer, you will be the architect of our security data ecosystem. Your primary mission is to design and build high-performance data lake architectures and real-time streaming pipelines that serve as the foundation for COGNNA's Agentic AI initiatives. You will ensure that our AI models have access to fresh, high-quality security telemetry through sophisticated ingestion patterns.

1. Data Lake & Storage Architecture

• Architectural Design: Design and implement multi-tier Data Lakehouse architectures to support both structured security logs and unstructured AI training data.
• Storage Optimization: Define lifecycle management, partitioning, and clustering strategies to ensure high-performance querying while optimizing for cloud storage costs.
• Schema Evolution: Manage complex schema evolution for security telemetry, ensuring compatibility with downstream AI/ML feature engineering.

2. Real-Time & Streaming Processing

• Streaming Ingestion: Build and manage low-latency, high-throughput ingestion pipelines capable of processing millions of security events per second in real-time.
• Unified Processing: Design unified batch and stream processing architectures to ensure consistency across historical analysis and real-time threat detection.
• Event-Driven Workflows: Implement event-driven patterns to trigger AI agent reasoning based on incoming live data streams.

3. AI/ML Enablement & Feature Engineering

• Vector Data Foundations: Architect the data infrastructure required to support semantic search applications and variants of RAG architectures for our generative AI models.
• Feature Management: Design and maintain a centralized repository for ML features, ensuring consistent data is used for both model training and real-time inference.
• AI Pipeline Orchestration: Build automated workflows to handle data preparation, model evaluation, and deployment within our cloud AI ecosystem.

4. DataOps & Systems Design

• Infrastructure as Code: Utilize declarative tools (e.g., Terraform) to manage the entire lifecycle of our cloud data resources and AI endpoints.
• Quality & Observability: Implement automated data quality frameworks and real-time monitoring to detect "data drift" or pipeline failures before they impact AI model performance.

• Experience & Education: 5+ years in Data Engineering or Backend Engineering, focused on large-scale distributed systems. B.S. or M.S. in Computer Science or a related technical field.
• Cloud Architecture: Deep architectural mastery of the Google Cloud Platform ecosystem, specifically regarding managed analytical warehouses, serverless compute, and identity/access management. Proven track record of deploying enterprise-scale Data Lakehouses from scratch.
• Real-Time Mastery: Expertise in building production-grade distributed messaging and stream processing engines (e.g., managed Apache Beam/Flink environments) capable of handling high-velocity telemetry.
• AI Enablement: Strong understanding of how data architecture impacts AI performance. Experience building embedding pipelines, feature stores, and automated workflows for model training and evaluation.
• Software Fundamentals: Expert-level Python and advanced SQL. Proficiency in high-performance languages like Go or Scala is highly desirable.
• Operational Excellence: Advanced knowledge of CI/CD, containerization on Kubernetes, and managing cloud infrastructure through code to ensure reproducible environments.

• Experience with dbt for modern analytics engineering.
• Understanding of cybersecurity data standards (OCSF/ECS).
• Previous experience in an AI-first startup or a high-growth security tech company.

💰 Competitive Package – Salary + equity options + performance incentives
🧘 Flexible & Remote – Work from anywhere with an outcomes-first culture
🤝 Team of Experts – Work with designers, engineers, and security pros solving real-world problems
🚀 Growth-Focused – Your ideas ship, your voice counts, your growth matters
🌍 Global Impact – Build products that protect critical systems and data