Data Engineer H/F

Alta Ares
Paris, France
On-site

Who this role is best for

Best suited to mid-level data engineers comfortable with defense sector constraints and real-time AI deployments.

Best fit for

  • Data engineers with production-grade PostgreSQL and GCP experience in defense or high-security domains.
    — “Experience deploying and operating pipelines on GCP or another cloud provider”
  • Candidates who can bridge data engineering and MLOps workflows for military applications.
    — “Guarantee reproducibility of datasets and integrate data pipelines into broader ML workflows”
  • Pragmatic builders prioritizing reliability in complex, high-volume data environments.
    — “You are a pragmatic Data Engineer with a strong focus on building reliable data systems”

Things to consider

  • Must adhere to defense-grade security protocols and data classification standards.
    — “Handle data classification and enforce security standards aligned with defense constraints”
  • Regular collaboration with ML teams on time-sensitive operational deployments.
    — “collaborating closely with ML teams in fast-paced environments”

How to stand out

  • Demonstrate specific optimizations for image/video pipelines in constrained environments.
    — “Experience working with image or video data pipelines”
  • Show concrete examples of security-conscious data governance implementations.
    — “Sensitivity to security, data governance, and defense-related constraints”
  • Highlight Prefect/Airflow pipeline monitoring strategies for mission-critical systems.
    — “Experience with monitoring, debugging, and optimizing data workflows”
Pace · SteadyCollaboration · HighAutonomy · MediumDecision Impact · TeamLevel · Mid

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • designing and maintaining data pipelines
  • deploying and operating pipelines on GCP
  • implementing access control mechanisms
Typical background
2–3 years of experience in Data Engineering

Skills & requirements

Required

Data EngineeringPythonSQLPostgreSQLWorkflow OrchestratorGCPData ModelingData StorageMlopsSecurityData Governance

Preferred

Image Or Video Data PipelinesMlops ConceptsEdge ComputingDefense-related Constraints

Stack & domain

PythonSQLPostgreSQLPrefectGCPData ModelingData StorageMl CollaborationMlops IntegrationSecurityData GovernanceDefense ConstraintsPragmaticFocus On Building Reliable Data SystemsComfortable Working With Complex, High-volume DatasetsCollaborating Closely With Ml TeamsData EngineeringDefenseAI

About the role

Original posting from Alta Ares via Ashby

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.

Our product suite includes:

  • 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

Source: Alta Ares careers (Ashby)

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