Data Engineer, AI – Distributed Systems

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Zignal Labs

Site WebLinkedInTous les Emplois

51 - 200 employés

Fondée en 2011

🎖️ Défense

💼 Conseil

📦 Logistique

💰 €15 000 000 Debt Financing - Zignal Labs en 2019-01

Defense • Consulting • Logistics

Zignal Labs est une entreprise technologique qui propose une plateforme d'intelligence agentique pour les utilisateurs de la sécurité nationale et de la défense. Sa plateforme offre des flux de travail persistants et dirigés par des analystes, qui collectent et enrichissent des signaux multimodaux à grande échelle, produisent des sorties de renseignement structurées et des alertes via des API, et permettent une alerte anticipée et un avantage décisionnel soutenu pour protéger les personnes, les installations et les missions dans les environnements politiques, militaires, économiques, sociaux, d'information, d'infrastructure et physiques.

Description

• Develop and operate batch and streaming pipelines that ingest and enrich high-volume unstructured data • Own pipeline components end to end, from implementation through production monitoring • Build and extend data pathways feeding NLP, LLM, and retrieval services • Perform data preparation, embedding generation, and indexing workflows • Integrate with and tune search and vector stores for semantic search, clustering, and real-time retrieval • Implement and improve microservices and APIs delivering analytics to enterprise customers • Write clean, tested, maintainable code • Participate in code review, CI/CD, and infrastructure-as-code practices • Debug production issues and improve reliability • Collaborate with Data Science, ML, Product, and Security teams to move ideas from prototype into production

🎯 Exigences

• 3+ years building and operating data pipelines in production • Strong programming skills in a JVM language: Scala, Java, or Kotlin • Working proficiency in Python for data and scripting work • Hands-on experience with a distributed processing framework, most likely Apache Spark • Hands-on experience with a streaming platform, most likely Kafka, including consumer groups, offsets, partitioning, and lag handling • Practical AWS experience and comfort with Docker • Ability to work in a Kubernetes environment • Experience with a workflow orchestrator such as Airflow, Prefect, or Dagster • Solid SQL and experience with at least one NoSQL or caching layer, such as Redis, MongoDB, or DynamoDB • Sound computer science fundamentals in data structures and algorithms • Ability to reason about performance and correctness in distributed systems • Strong written communication and ability to work asynchronously across U.S. time zones

🏖️ Avantages

• Fully remote work • Support for learning Scala for candidates strong in Java or Kotlin • Opportunity to work alongside experienced engineers running systems at scale