Data Engineer H/F

Alta Ares; Paris; Sur place; EUR 50 000 - 70 000; Plein temps

Résumé du poste

Alta Ares, based in Paris, is seeking a pragmatic Data Engineer focused on building reliable data systems. You'll design and maintain data pipelines and orchestrate workflows using tools like Prefect while ensuring scalability and reliability.

The ideal candidate has 2–3 years of Data Engineering experience, strong proficiency in Python and SQL, and familiarity with GCP. This role is pivotal, collaborating closely with ML teams in a fast-paced defense-related environment.

Qualifications

• 2–3 years of experience in Data Engineering.
• Hands-on experience with a workflow orchestrator (Prefect, Airflow, or Dagster).
• Experience deploying and operating pipelines on GCP or another cloud provider.

Responsabilités

• Design and maintain batch and near real-time data pipelines.
• Deploy and operate data pipelines on GCP.
• Implement access control mechanisms and manage data permissions.

Connaissances

Python for data processing

SQL skills

PostgreSQL experience

Workflow orchestrator experience

GCP or cloud provider knowledge

Monitoring and debugging data workflows

Description du poste

About Alta Ares

Alta Ares is a deeptech startup founded in 2024, building real-time AI for defense operations — ISR, C-UAS, and autonomous systems. Our clients are NATO‑aligned militaries and defence institutions, with regular deployments during live exercises and operational demonstrations. We raised €2M seed in May 2025, €50M in June 2026, and are expanding fast.

Product Suite

• Real‑time AI ISR module deployable on drones and tactical platforms
• Counter‑UAS solution enabling autonomous drone takeover in GNSS‑denied environments
• Full‑stack MLOps platform for training & deploying military‑grade AI models
• Data fusion and trajectory prediction

Role & Mission

Data Pipelines & Orchestration

Design and maintain batch and near real‑time data pipelines across multiple sources (APIs, files, sensors, partners). Orchestrate workflows using Prefect (or similar tools) and ensure reliability, scalability, and observability of data workflows.

Data Infrastructure (GCP)

Deploy and operate data pipelines on GCP (Compute Engine, Cloud Run, Cloud SQL, GCS). Manage data flows between object storage and relational databases, while optimizing performance, cost, and monitoring of production workloads.

Data Modeling & Storage

Design and implement PostgreSQL schemas adapted to analytical and ML use cases. Define dataset versioning strategies and ensure data quality, consistency, and traceability across systems.

ML Collaboration & MLOps Integration

Prepare and expose datasets for ML training pipelines. Guarantee reproducibility of datasets and integrate data pipelines into broader ML workflows and MLOps systems.

Security & Governance

Implement access control mechanisms and manage data permissions. Handle data classification and enforce security standards aligned with defense constraints.

Requirements

Candidate profile

You are a pragmatic Data Engineer with a strong focus on building reliable data systems in production. You are comfortable working with complex, high‑volume datasets (including images, videos, and logs) and collaborating closely with ML teams in fast‑paced environments.

• 2–3 years of experience in Data Engineering
• Strong proficiency in Python for data processing
• Solid SQL skills (data modeling, query optimizatio
• Experience with PostgreSQL in production environments
• Hands‑on experience with a workflow orchestrator (Prefect, Airflow, or Dagster)
• Experience deploying and operating pipelines on GCP or another cloud provider
• Ability to design robust, maintainable, and scalable data pipelines
• Experience with monitoring, debugging, and optimizing data workflows

Nice to Have

• Experience working with image or video data pipelines
• Familiarity with MLOps concepts and tooling
• Experience in constrained environments (edge computing, offline systems)
• Sensitivity to security, data governance, and defense‑related constraints