Senior Software Engineer – Satellite Data Processing

Satlantis US•Gainesville, FL

3d•Onsite

About The Position

We are seeking a highly motivated and experienced Senior Software Engineer with strong technical leadership to spearhead our satellite data processing platform. The ideal candidate will possess a strong background in software development, with a specific focus on handling large datasets and running complex, computationally intensive image processing algorithms at scale. This is a hands-on role where you’ll own backend services, ETL/ELT pipelines, and large-scale processing that power AI and satellite imagery workflows. You’ll set engineering standards, mentor teammates, and ship reliable systems that move data from raw capture to insight—fast. This role requires a deep understanding of software development and data processing algorithms, as well as leadership skills and the ability to work collaboratively in a fast-paced and innovative environment.

Requirements

Bachelor’s or Master’s degree in Computer Science or a related field.
3+ years of professional experience building production backend systems with measurable uptime, performance, and security.
Language expertise: Python, with TypeScript/Go/C++/Rust as a plus. You write production-grade, well-tested services.
Asynchronous I/O proficiency: deep understanding of async programming models (e.g., Python asyncio/uvicorn, Node.js async/await), including concurrency, back-pressure, and non-blocking I/O.
RESTful API development: hands-on design and operation of service-oriented and microservice architectures; RESTful API design at scale (resource modeling, pagination, versioning, idempotency), schema/contract testing, authN/Z, and backward-compatible evolution.
ETL/ELT orchestration: hands-on production experience with Airflow, Prefect, Dagster, Temporal, and/or other ETL/ELT orchestration systems (expert in at least one).
Data & storage: strong SQL (e.g., PostgreSQL), schema design and migrations; familiarity with non-relational stores and object storage for large artifacts.
Cloud & DevOps: proven experience on AWS/GCP/Azure, containers, Kubernetes, CI/CD, and IaC (Terraform/Terragrunt).
Observability & quality: metrics/logging/tracing, incident management, automated tests, and CI quality gates.
Technical leadership: demonstrated project/team leadership, mentoring, roadmap ownership, and cross-functional influence.
Work Authorization: This role does not offer sponsorship for employment visas. Candidates must have and maintain unrestricted legal authorization to work in the U.S. now and in the future, without requiring employer-sponsored visa support.

Nice To Haves

FastAPI for high-performance Python services (dependency injection, Pydantic models, async endpoints).
Geospatial/satellite domain: GDAL/Rasterio, STAC/PgSTAC, GeoTIFF/COG/NetCDF5, and large imagery pipelines.
Workflow/eventing: Argo Workflows, Kafka/NATS/SQS; data quality & lineage (Great Expectations, OpenLineage).
AI/ML productionization: PyTorch/TensorFlow inference services, model packaging, GPU scheduling, and monitoring.
Performance engineering: vectorized/GPU compute, caching strategies, and cost/perf tuning at scale.
Frontend: React/TypeScript for internal dashboards and operator tools.

Responsibilities

Lead by example. Design, build, and maintain high-throughput services and APIs that ingest, transform, and serve imagery at scale—so scenes move from capture to analysis-ready products in minutes, not hours.
Own ETL/ELT orchestration. Translate data/algorithm requirements into robust DAGs with Airflow/Prefect/Dagster/Temporal—including sensors, retries/backfills, data quality checks, SLAs, and lineage—so pipelines are observable, idempotent, and easy to operate.
Architect for scale and resilience. Define clear service boundaries and data contracts (OpenAPI/JSON Schema), and evolve services with versioned APIs, safe migrations, and cost-aware designs.
Integrate AI the right way. Wrap models behind stateless inference services, manage artifacts and rollouts (A/B, canary), and ensure gradients of observability (latency, drift, throughput) for quick iteration.
Raise the engineering bar. Drive design and code reviews, enforce testing strategy (unit/integration/e2e), and embed secure-by-default practices (authN/Z, secrets, least privilege).
Mentor and multiply. Coach juniors and interns, lead technical onboarding, and shape hiring needs—so the team scales without losing speed or quality.
Deliver through DevOps. Own Infrastructure as Code with Terraform/Terragrunt, Kubernetes manifests/Helm deployments, and environment promotion (dev, staging, prod).
Partner across functions. Collaborate with the CTO, product, and science teams to translate research and customer needs into scoped roadmaps and ensure on-time deliveries.
Unblock end-to-end delivery. Contribute to internal tools and client UIs in React/TypeScript when needed (e.g., pipeline health dashboards, operator controls).