• Define and maintain enterprise-wide logical and physical data models • Establish and enforce scalable data standards and governance policies • Ensure our data platform is reliable, high-performing, and built for growth • Align data architecture with long-term business and product strategy • Design and refine conceptual, logical, and physical data models • Optimize database schemas for performance, maintainability, and scalability • Lead strategic database refactoring and modernization initiatives • Architect secure, cloud-native data solutions, with a strong emphasis on Azure • Design high-availability, disaster recovery, and resilience strategies • Guide the evolution of our modern data ecosystem • Design and oversee resilient ETL/ELT pipelines for ingestion and transformation • Establish standards for API integrations and data exchange • Ensure reliable capture, processing, and distribution of enterprise data • Implement comprehensive data governance frameworks • Define and enforce role-based access controls and data security standards • Ensure compliance with regulatory requirements such as HIPAA, FERPA, and SOC 2 • Establish audit logging and traceability practices to protect data integrity • Partner closely with Product and Engineering teams to support new feature development • Provide technical leadership and architectural direction to data engineers and developers • Conduct architecture reviews and approve data design specifications • Mentor team members on best practices in data architecture and governance

• Diseñar, desarrollar y mantener canales de datos y procesos ETL para garantizar el flujo eficiente y confiable de datos desde diversas fuentes a nuestro almacén de datos. • Implementar procesos de validación y calidad de los datos para garantizar la precisión e integridad de los datos. • Realizar modelado de datos y diseño de bases de datos para optimizar el almacenamiento y la recuperación de datos. • Optimizar el rendimiento y la escalabilidad de una base de datos para manejar volúmenes masivos de datos. • Supervisar y solucionar problemas de sistemas y canalizaciones de datos para identificar y resolver cualquier problema o cuello de botella. • Desarrollar y mantener documentación para procesos, sistemas y mejores prácticas de ingeniería de datos. • Colaborar con equipos interdisciplinarios para garantizar la integridad y seguridad de los datos.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description As a Geospatial Data Platform / Label Ops Engineer on the AI/Advanced Engineering team, you’ll own the imagery and labeling data plane behind SkyFi’s near-real-time satellite analytics, making diverse partner imagery fast to ingest, consistent to use, and reproducible end-to-end. You’ll build and operate scalable pipelines to normalize and catalog imagery across many sensors/providers, deliver high-performance tiling/chipping and retrieval services for training and inference, and implement dataset + label versioning and lineage so every model output and evaluation result can be traced back to the exact data used. You’ll define and maintain our labeling pipeline with QA/adjudication and auditability. Working closely with CV and runtime owners, you’ll ship self-serve data products that speed up iteration and improve accuracy. This is a high ownership position where you’ll be a cornerstone member of a team that is empowering the future of Geospatial AI. Qualifications - Demonstrated experience building geospatial imagery pipelines at scale (raster workflows, tiling/chipping, handling heterogeneous sensors/metadata). - Strong data engineering fundamentals: idempotency, backfills, observability, SLAs, schema evolution, and production reliability. - Experience building internal data APIs/SDKs and treating data as a product. - Hands-on experience with labeling workflows or data QA at scale (vendor coordination, task design, QA/adjudication mechanics). - Ability to collaborate tightly with CV/eval owners to translate failure modes into actionable data/labeling pipelines. Requirements - Own the imagery data plane: ingest, normalize, catalog, and serve imagery + metadata across diverse sources for near-real-time and batch workloads. - Build and operate tiling/chipping + retrieval services optimized for training and NRT inference (spatial/temporal indexing, caching, precompute, and latency SLAs). - Implement dataset and label versioning + lineage so every model run / evaluation can be reproduced. - Build and run label ops workflows: task generation, QA, adjudication, gold-check insertion, audit-ability, throughput tracking. - Create data products for internal consumers (APIs/services) that let CV engineers self-serve imagery chips, labels, and eval sets. - Build robust backfill/reprocessing pipelines (idempotent, observable, safe incremental recompute) to support new analytics and changing requirements. - Establish data health monitoring (freshness, completeness, corruption, sensor distribution drift, metadata validation) with alerts and dashboards. - Partner with evaluation and runtime owners to close the loop of failure buckets -> labeling requests -> dataset versions -> retraining/eval. - Partner with computer vision researchers to define image and label strategies for new projects. - Responsible for making sure everyone has the images/data/labels they need. Benefits - Be well compensated. Possibility for equity. - Receive best-in-class benefits, including premium medical, dental, and vision coverage and 20 days paid time off. - Play a critical role in building a market-changing product in the exciting realm of Space. - Thrive in a fast-paced, dynamic environment that rewards initiative, innovation, and getting things done. Salary Band $180,000–$220,000 USD base salary

• design, build, and maintain scalable data pipelines and ingestion frameworks • deliver high value POCs to stabilize and build a strong foundation for an enterprise data platform • develop custom ingestion, optimize data workflows • ensure reliable data delivery into Snowflake or other cloud-based platforms • collaborate with analytics, product, and engineering teams to enable data-driven decision-making