V
Data Engineer
לא צויןלא צויןData EngineeringCybersecurity
Build and operate the data foundations that enable production AI models and agents for a cybersecurity platform. Partner with AI engineers and researchers to deliver reliable, scalable data infrastructure.
תחומי אחריות
- Design, build, and operate scalable batch and streaming data pipelines for production AI systems
- Own ingestion, storage, transformation, serving, monitoring, and continuous improvement across the data lifecycle
- Build data layers that make raw data semantically accessible to AI models and agents
- Implement monitoring, alerting, and validation checks to maintain data quality and operational reliability
- Work with data lakes, warehouses, relational, graph, and vector databases for diverse AI workloads
- Partner with AI engineers and researchers to translate model requirements into production-grade infrastructure
- Define and evolve the data strategy for AI applications
דרישות
- 4+ years of professional experience in data engineering or ML infrastructure
- Experience designing and building batch and streaming pipelines for large-scale production systems
- Hands-on experience with data lakes, data warehouses, relational databases, and graph databases
- Ability to build reliable, observable, and scalable infrastructure with monitoring, alerting, and data quality checks
- Ownership of the full data lifecycle, including ingestion, modeling, serving, monitoring, and continuous improvement
- Experience with technologies such as DuckDB, dbt, Temporal, Trino, Spark, PostgreSQL, PGVector, Neo4j, Datadog, Python, Go, Docker, and Kubernetes
- Experience using ML models and frameworks in data pipelines, including embeddings, vector databases, or semantic search
יתרון
- Experience building ML/AI data infrastructure, including feature extraction and inference-time data access
- Experience with high-volume or complex data such as logs, events, telemetry, or security data
- Familiarity with AWS and cloud-native data tools
- Experience translating ML and research requirements into scalable data solutions