Head of Data Engineering

We are looking for an experienced, hands-on Head of Data to lead the design, development, and deployment of a scalable data platform capable of handling billions of events and connections every day. This platform will serve as the foundation for our advanced cybersecurity solutions, enabling real-time data processing, complex modeling, machine learning workflows, and large language model (LLM) integration.

Key Responsibilities:

• Data Architecture & Engineering: Design and implement the core data infrastructure, including data lakes, warehouses, and real-time streaming systems.
• Pipeline Development: Build and maintain robust pipelines to ingest, process, and transform large, diverse datasets while ensuring quality and consistency.
• Database Strategy: Oversee database implementation and optimization, including indexing, partitioning, aggregations, and high-performance query execution.
• Scalability & Reliability: Ensure the platform performs efficiently at scale, handling massive daily volumes with minimal latency.
• ML & LLM Integration: Partner with data science teams to deploy and optimize machine learning pipelines and integrate LLMs into production workflows.
• Cross-Functional Collaboration: Work closely with engineers, data scientists, and cybersecurity experts to align the platform with research and business goals.
• Innovation & Technology Leadership: Keep the team at the forefront of emerging trends in big data, cloud platforms, and cybersecurity to continuously improve the platform.

Required Skills & Experience:

• Large-Scale Data Systems: Proven experience designing and building complex data architectures on cloud platforms such as AWS, GCP, or Azure.
• Data Modeling Expertise: Strong hands-on experience developing sophisticated data models and schemas for both batch and real-time processing.
• Backend Development: Extensive experience developing backend systems for high-throughput, data-driven applications.
• Streaming Data Technologies: Proficiency with tools like Apache Kafka, Flink, or Spark Streaming for processing high-velocity data streams.
• Database Management: In-depth knowledge of SQL, NoSQL, NewSQL, and graph databases, with experience optimizing queries and operations at scale.
• Machine Learning & LLMs: Experience running production ML pipelines and integrating LLMs into data systems.
• Data Security & Governance: Familiarity with encryption, data governance, and security best practices for handling sensitive information.
• Leadership & Collaboration: Ability to lead cross-functional teams and communicate complex technical concepts to both technical and non-technical stakeholders.