Senior Data Engineer (MLOps / Data Pipelines)

About the role

We are seeking an experienced Senior Data Engineer to join a fast‑growing geospatial analytics platform that processes satellite imagery and environmental data at scale. You will help evolve existing pipelines into secure, automated production systems while collaborating with data scientists and engineers.

Key responsibilities

• Design, optimize, and maintain scalable MLOps and data‑pipeline infrastructure.
• Automate data ingestion, processing, and deployment workflows using modern MLOps practices.
• Operationalize machine‑learning pipelines, improving reliability, monitoring, and performance.
• Enhance CI/CD processes, containerized deployments, and cloud‑based infrastructure.
• Evaluate and improve architecture for scalability, security, and cost efficiency.
• Integrate new data sources and optimize large‑scale processing workloads.
• Collaborate with cross‑functional teams, mentor junior engineers, and drive engineering best practices.

Required profile

• 5+ years of experience in MLOps, data engineering, or platform engineering.
• Strong expertise in Python and SQL (PostgreSQL preferred).
• Hands‑on experience with Docker, Kubernetes, CI/CD, and cloud platforms.
• Experience building and managing production ML/data pipelines.
• Familiarity with GIS, satellite imagery, PostGIS, and Google Earth Engine.
• Experience with large‑scale datasets, distributed processing, and event‑driven architectures.

Required skills

• Python
• SQL
• PostgreSQL
• Docker
• Kubernetes
• CI/CD
• Airflow
• Kafka
• dbt
• GIS
• PostGIS
• Google Earth Engine
• Redis

What we offer

• Friendly, competitive, and creative work environment with international leadership exposure.
• Hybrid work schedule (standard 5‑day week, 9:00 AM – 5:30 PM).
• Competitive salary with performance‑based bonuses and regular reviews.
• Access to paid learning platforms and modern technologies.
• Employee benefits including Provident Fund, paid leave, sick leave, and team‑building activities.