Senior Geospatial Data Engineer
Doktar
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Benefits offered by this job
Job summary
Doktar’da Senior Geospatial Data Engineer rolü için Istanbul’da hibrit çalışma modeliyle uygun aday aranıyor. Görev, büyük ölçekli uydu ve çevresel verileri işleyip güvenilir analitik ürünlere dönüştürmek üzere jeo-uzamsal veri boru hatlarını tasarlamayı ve işletmeyi kapsar.
Bu rol, Python/SQL ve PostgreSQL/PostGIS konusunda olgun deneyim, GDAL/Rasterio/GeoPandas gibi araçlarla üretim odaklı çözümler üretme becerisi gerektirir; bulut tabanlı iş akışları ve CI/CD süreçlerine aşinalık beklenir.
Qualifications
- Coğrafi veri mühendisliğinde en az 5+ yıl tecrübe ve net teknik sahiplik.
- Python ve SQL ile üretim verisi boru hatları geliştirme tecrübesi.
- PostgreSQL/PostGIS üzerinde geniş ölçekli konum verisi tasarımı ve indexing bilgisi.
- GDAL, Rasterio, GeoPandas gibi araçlarda tecrübe; bulut tabanlı veri iş akışlarına aşinalık.
- Uyumlu ve güvenilir üretim süreçleri için test, sürüm kontrol, izlenebilirlik bilgisi.
Responsibilities
- Uydu, iklim ve çevresel verileri içe aktarma, işleme ve sunma için ölçeklenebilir boru hatları tasarla/detene ed.
- Tarım, su, iklim ve sürdürülebilirlik uygulamaları için üretim kalitesinde jeo-uzamsal iş akışları geliştir
- CropMap jeo-uzamsal veritabanını yönetin ve performans, ölçeklenebilirlik ve veri bütünlüğünü sağlayın
- Python, SQL ve jeo-uzamsal teknolojileri kullanarak raster/vector/time-series işleyişlerini yönetin
Skills
Tools
Job description
Anasayfa
Kariyer
Senior Geospatial Data Engineer
Hybrid Istanbul
Join Doktar, where we build satellite-driven data and analytics systems for agriculture, sustainability, and water security.
As a Senior Geospatial Data Engineer, you will design, build, and operate scalable geospatial data pipelines that transform large volumes of satellite, climate, and environmental data into reliable analytical products. You will work with datasets such as Sentinel, Landsat, MODIS, GRACE, ERA5, and CHIRPS to support basin-scale water assessments, climate-risk analytics, groundwater and evapotranspiration analysis, and agricultural intelligence.
This role combines geospatial engineering, Earth observation, and cloud-based data processing. You
will collaborate with geospatial analysts, data scientists, machine learning engineers, and domain experts to take analytical methods from development into robust, reproducible, and production- ready workflows.
The role is well suited to someone who enjoys solving complex data-engineering problems and building dependable systems from large, heterogeneous Earth observation datasets.
Design, build, and maintain scalable pipelines for ingesting, processing, and serving satellite, climate, and environmental data.
Develop production-grade geospatial workflows for agricultural, water, climate, and sustainability applications.
Manage and continuously improve the CropMap geospatial database, ensuring its performance, scalability, reliability, and data integrity.
Process and manage large raster, vector, and time-series datasets using Python, SQL, and geospatial technologies.
Build reusable data-processing components and automate workflows for analyses such as evapotranspiration, water balance, drought, groundwater, and crop water stress.
Design efficient geospatial data models, storage structures, metadata standards, and data- access patterns.
Improve the performance, reliability, observability, and cost efficiency of geospatial processing workloads.
Implement testing, validation, versioning, monitoring, and quality-control mechanisms across geospatial data pipelines.
Operationalize methodologies developed by geospatial analysts, data scientists, and environmental specialists.
Develop services, APIs, and data products that make geospatial outputs accessible to downstream applications and analytical teams.
Establish engineering standards for reproducibility, documentation, code quality, and deployment.
Provide technical guidance through architecture decisions, code reviews, and mentoring of less- experienced team members.
Work across AI/ML, geospatial, product, and sustainability initiatives to convert environmental data into dependable operational insights.
Typically 5+ years of relevant experience in geospatial data engineering, geospatial software engineering, Earth observation systems, or large-scale geospatial data processing, with demonstrated senior-level technical ownership.
Strong Python and SQL skills, with experience developing maintainable, tested, and production- ready data pipelines.
Advanced hands-on experience with PostgreSQL/PostGIS, including spatial database design, indexing, query optimization, and management of large geospatial datasets.
Strong experience with geospatial technologies such as GDAL, Rasterio, GeoPandas, Xarray/Rioxarray, QGIS, or Google Earth Engine.
Solid understanding of raster and vector processing, spatial databases, coordinate reference systems, map algebra, spatial indexing, and geospatial data quality.
Experience working with satellite and climate datasets such as Sentinel, Landsat, MODIS, GRACE, ERA5, CHIRPS, or comparable Earth observation sources.
Experience designing and operating cloud-based data workflows on Azure, AWS, or Google Cloud.
Hands-on knowledge of Docker, workflow orchestration, CI/CD, automated testing, and production monitoring.
Experience with scalable or distributed processing technologies such as Dask, Spark, Ray, or cloud-native geospatial processing frameworks.
Familiarity with modern geospatial formats and standards such as Cloud-Optimized GeoTIFF, Zarr, GeoParquet, STAC, and spatial APIs.
Ability to optimize processing pipelines for performance, storage efficiency, reliability, and cloud cost.
Ability to translate analytical requirements into sound data architecture and production systems.
Clear technical communication skills, including architecture documentation, methodology notes, and stakeholder-facing explanations.
Strong ownership, analytical thinking, and the ability to make sound technical decisions in a collaborative environment.
Experience with agriculture, hydrology, water balance, evapotranspiration, drought, groundwater, sustainability, or climate-risk applications.
Experience operationalizing remote-sensing algorithms or machine-learning models.
Familiarity with Kubernetes, infrastructure as code, data catalogues, or geospatial API development.
Experience providing technical leadership, mentoring engineers, or defining engineering standards.
A rewarding role within a young and dynamic interdisciplinary team
Opportunities for professional growth and career advancement
A blend of in-office and remote working, ensuring work-life balance
Private health insurance that also covers family members below 22 years
Daily meal and transportation allowance
Attractive yearly bonuses based on performance
Employee Stock Option Scheme
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