Software/Data Engineer - Geospatial Algorithms
Role details
Job location
Tech stack
Job description
- Demonstrated familiarity with software engineering practices specifically applied to geospatial data processing and the development of spatial algorithms at scale
Requirements
- Strong understanding of vector geospatial data products and their associated processing pipelines, including ingestion, transformation, and quality validation workflows
- Thorough understanding of modern software engineering principles and cloud-native environments, with the ability to design and deploy scalable, production-grade geospatial solutions
Qualifications/Skills Required:
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TS/SCI with CI Poly required
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Must be willing to work in SCIF daily or as needed
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5+ years of professional software engineering experience with cloud-first development approach
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5+ years of hands-on experience with AWS CDK for Infrastructure as Code
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3+ years building complex, reusable CDK constructs and stacks
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Proficiency in async programming and performance optimization* 3+ years implementing multi-environment deployment strategies using CDK
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5+ years of advanced Python development experience
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4+ years of data engineering experience
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ETL/ELT pipeline development using AWS services
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Experience with data lakes, data warehousing, and analytics platforms o Real-time data processing and streaming architectures
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3+ years with AWS cloud-native services o Advanced knowledge of Lambda, API Gateway, EventBridge, SQS, SNS o Experience with data services: RDS, DynamoDB, Redshift, S3, Athena o Container orchestration with ECS, Fargate, or EKS
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Strong problem-solving abilities with proven track record of resolving complex technical challenges, * 3+ years of software engineering experience with a focus on geospatial data processing and spatial algorithm development
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Hands-on experience working with vector geospatial data products and familiarity with common processing workflows, including ingestion, transformation, and quality validation
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Strong command of modern software engineering principles and cloud-native environments, with demonstrated ability to build and deploy scalable, production-grade geospatial solutions