Production platform · Climate LLC / Bayer Crop Science
Reliable systems. Seasonal scale.
Model execution and geospatial delivery across millions of acres.
AI-generated editorial artwork; not a project screenshot or research result.
01 / Context
The problem worth solving.
Recommendation and modeling teams need reliable access to agricultural models and geospatial data across continental operations, with demand that rises sharply during seasonal production windows.
02 / My contribution
Where I made a difference.
I architect backend and data services across AWS and GCP, lead WunderGraph-based GraphQL Federation, and shape versioned REST contracts. My work connects agricultural model execution, geospatial data access, and the operational controls needed for seasonal demand.
03 / System design
Connecting the pieces.
- 01REST & federated GraphQL
- 02Asynchronous model execution
- 03Geospatial data & BigQuery
- 04Telemetry & operational controls
04 / Engineering tradeoffs
The decisions behind the system.
API evolution without disruption
Federated schemas let teams own their services, but changes can affect consumers beyond a single team. Backward-compatible contracts, schema checks, and staged releases reduce integration and rollout risk.
Recovery as part of the workflow
Asynchronous execution separates request handling from long-running work. Batching, dead-letter queues, checkpoints, and idempotent handling make partial failures recoverable and reprocessing safe.
Data access with a cost boundary
BigQuery makes analytical data accessible across workflows. Partition-aware queries and reporting patterns avoid unnecessary scans and reduce cost exposure.
05 / Outcome & evidence
Work you can evaluate.
The platform supports model execution and geospatial data delivery across millions of acres annually. Backward-compatible contracts, schema governance, and reliability practices keep high-concurrency seasonal workloads predictable and observable.
Public-safe account of my contribution. Bayer’s website supplies organizational context; it does not independently verify internal implementation details. No proprietary code, diagrams, or operational measurements are reproduced.