Data Engineer: Production Pipelines & ML Ops on GCP

Alta Ares; Paris; Sur place; EUR 50 000 - 75 000; Plein temps

Résumé du poste

Alta Ares is seeking a pragmatic Data Engineer to design and maintain batch and near real-time data pipelines across APIs, files, sensors, and partners. You will orchestrate workflows with Prefect (or similar) and ensure reliability, scalability, and observability of data workflows in production.

You will deploy and operate data pipelines on GCP, manage data flows between storage and relational databases, and collaborate with ML teams to integrate data pipelines into ML workflows and MLOps.

Qualifications

• 2–3 years of experience in Data Engineering.
• Strong proficiency in Python for data processing.
• Solid SQL skills including data modeling and query optimization.
• 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.

Responsabilités

• 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.
• 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 and cost.

Connaissances

Python

SQL

Data pipelines

Monitoring and debugging

Cloud data platforms

Outils

Prefect

Airflow

Dagster

PostgreSQL

GCP

Description du poste

Alta Ares is seeking a pragmatic Data Engineer to design and maintain batch and near real-time data pipelines across APIs, files, sensors, and partners. You will orchestrate workflows with Prefect (or similar) and ensure reliability, scalability, and observability of data workflows in production.

You will deploy and operate data pipelines on GCP, manage data flows between storage and relational databases, and collaborate with ML teams to integrate data pipelines into ML workflows and MLOps.