OPEN POSITION
Responsibilities
Design and build scalable data pipelines for processing satellite imagery and geospatial data
Develop and maintain data infrastructure supporting Solafune's platform and analytics products
ETL/ELT pipeline development
Data warehouse architecture and optimization
Real-time data processing systems
Implement data quality monitoring and validation frameworks
Optimize data storage and retrieval systems for large-scale geospatial datasets
Collaborate with AI/ML engineers to support model training and deployment workflows
Manage and optimize cloud-based data infrastructure (AWS, GCP)
Required Skills
2+ years of professional experience in data engineering or related fields
Strong proficiency in Python for data processing and pipeline development
Experience with SQL and database management (PostgreSQL, data warehouses)
Hands-on experience with cloud platforms (AWS or GCP)
Experience with workflow orchestration tools (Airflow etc.)
Experience with containerization (Docker) and version control (Git/GitHub)
Reading and writing skills in English (internal documentation is in English)
Preferred skills and experience (Nice to have)
Business-level English proficiency
Experience with geospatial data formats and tools (GeoTIFF, COG, STAC, GDAL, Rasterio)
Knowledge of distributed computing frameworks (Spark, Dask, Ray)
Experience with streaming data platforms (Kafka, Kinesis)
Familiarity with machine learning workflows and MLOps practices
Experience with Infrastructure as Code (Terraform, CloudFormation)
Knowledge of data modeling and dimensional design
Experience processing satellite imagery or remote sensing data
Background in working across multiple domains (e.g., DevOps, ML engineering)
Tools
Cloud platforms: AWS, GCP, Terraform
Programming languages: Python, SQL
Data orchestration: Airflow, Prefect, or similar
Databases: PostgreSQL, Redshift, BigQuery
Container management: Docker, Kubernetes
Version control: GitHub
Geospatial libraries: GDAL, Rasterio, GeoPandas, Xarray
Project management: GitHub, Notion
Internal communication: Slack, Discord, Notion, Zoom
Benefits
Hiring Process
① Resume Screening
Submit your resume, including relevant experience, education, and technical skills.
Highlight your experience with data engineering and any relevant projects or portfolios.
② Technical Assignment (1–2 hours)
Complete a short coding task or data science challenge to demonstrate your skills and problem-solving approach.
You may be asked to submit your code or discuss your solution briefly with the team.
③ Initial Interview (30 min – 1 h)
Discuss your technical background, research experience, and approach to data science.
You may also talk about your familiarity with tools, frameworks, and collaboration in a research and development team.
④ Team Interview (1–1.5 hours)
Meet with members of the research and development team and relevant stakeholders.
Discuss your research practices, code review experience, and how you collaborate in development cycles.
⑤ Reference Check (if applicable)
Provide references from previous employers or colleagues who can speak to your technical and teamwork abilities.
⑥ Offer and Onboarding
Receive and review the job offer, including role details, compensation, and benefits.
Complete onboarding procedures and begin your role as a Data Engineer with guidance from the team.
Work Conditions
Job Type
Full-time (after 3-month trial period) Outsourcing contracts are acceptable (for freelancers)
Work Type
Operate remotely or from the office
Location
Operate remotely or from the office
Salary
7,000,000 yen/ year or more (May vary depending on your skillset, experience and location)
Work Hours
9:00 - 18:00 (including 1 hour break) Five-day work week
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CAREER
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