senior-software-engineer@spiio:~/career
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AgTech IoT Startup

Senior Software Engineer

Spiio 🇩🇰 Copenhagen, Denmark
📅 May 2021 → June 2021 (2 months)

$ echo $IMPACT_METRICS

1,000+ Sensors
40K/hr Data Points
IoT Platform

$ cat tech-stack.json

⚡ Core Technologies

Node.jsPythonReact

🔧 Supporting Stack

InfluxDBMongoDBFirestoreMQTTRabbitMQCelery

☁️ Infrastructure & DevOps

DockerKubernetesGCPCI/CD

$ cat README.md

Spiio builds precision agronomy solutions using wireless in-ground soil sensors that measure moisture, temperature, salinity, and light levels in real-time.

I joined to accelerate development of their IoT platform, focusing on data pipeline optimization and microservice architecture improvements.

$ git log --oneline responsibilities/

→
Enhanced data pipelines processing data from 1,000+ wireless soil sensors generating 40,000+ hourly data points
→
Implemented MQTT-based sensor ingestion with RabbitMQ/Celery for reliable message processing
→
Managed time-series data in InfluxDB for high-volume IoT storage
→
Developed React dashboards for visualizing sensor data and agronomic insights
→
Deployed on Kubernetes/GCP for scalable, resilient architecture

$ grep -r "achievement" ./

✓
Improved data pipeline reliability for 1,000+ sensor network
✓
Boosted ingestion throughput with microservice patterns and Kubernetes scaling
✓
Built analytics tools enabling agronomy professionals to visualize hourly field data
✓
Strengthened cloud resiliency with automated deployment and monitoring strategies

$ cat CHALLENGES.md

High-Volume Time-Series Ingestion

🔴 Challenge:

40,000+ data points per hour from distributed sensors needed reliable ingestion and storage.

🟢 Solution:

Implemented MQTT for sensor communication with RabbitMQ buffering. Used InfluxDB's time-series optimization for efficient storage and querying.

MQTTRabbitMQInfluxDBNode.js

$ cat details.md

IoT at Scale

Working with IoT systems presents unique challenges:

  • Unreliable connectivity from field sensors
  • High data volumes requiring efficient storage
  • Real-time processing for actionable insights
  • Edge computing considerations

This short engagement gave me valuable exposure to time-series databases and IoT protocols.

Technologies: Python, PostgreSQL, AWS, Docker/Kubernetes

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