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

• Design, implement, and optimize data engineering architectures and processes, ensuring scalability, security, and efficiency. • Develop and implement data engineering solutions in Azure environments (Data Factory, Synapse Analytics, Databricks, Data Lake, SQL Database). • Build high-performance, reliable data pipelines (ETL/ELT). • Work on data modeling, system integration, and information governance. • Ensure best practices for performance, security, and scalability. • Collaborate with multidisciplinary teams and international stakeholders. • Participate in meetings and presentations in advanced English.

• Build, test, and document Snowflake data models and business logic in dbt • Apply and improve data quality, testing, observability, and lineage standards • Collaborate with cross-functional partners to define data contracts and interfaces • Contribute to Capital Rx’s modular data platform and client-specific data configurations • Participate in design reviews; propose scalable, maintainable patterns • Monitor pipeline health, troubleshoot incidents, and drive root-cause fixes • Optimize cost and performance of jobs, storage, and queries with guidance • Write clear documentation and support knowledge sharing • Adhere to the Capital Rx Code of Conduct, including reporting of noncompliance