Senior Software Engineer, Data Platform

At IUNU ("you knew"), our mission is to deliver confidence at scale to the commercial greenhouse industry. We built LUNA, a computer vision platform that autonomously tracks plant development to turn visual data into high-value decisions. Deployed in over 15 countries, our technology empowers growers with critical insights like yield forecasting and pest detection to drive operational efficiency and reduce waste.

We are looking for a Senior Engineer who is passionate about applying their technical expertise to solve real-world problems and build a more sustainable future for agriculture.

About the Role:

This role is a core systems engineering position for a builder who wants to solve complex challenges at the intersection of distributed systems and horticultural science. You will leverage your experience with data, algorithms, graphs, mathematics, and software engineering to enhance and extend the core of our LUNA system with robust, scalable systems. Working closely with our computer vision team and staff horticulturalists, you’ll transform the image and sensor data we gather to generate unique insights for growers.

Responsibilities:

• Design and implement the core distributed data processing engine that powers IUNU’s platform, moving beyond simple aggregation to handle large-scale datasets with high dimensionality.

• Optimize for performance & scale by implementing advanced concurrency patterns and algorithmic techniques to maximize throughput across our distributed compute environment.

• Design deterministic, event-driven workflows that guarantee data integrity and exactly-once processing, handling back pressure and late-arriving data in a non-deterministic physical world.

• Drive the technical direction of the team by championing rigorous design reviews, observability best practices, and fault-tolerant architecture that balances speed of execution with long-term system stability.

• Partner with the product and computer vision teams to translate abstract horticultural requirements into concrete, scalable technical solutions that directly impact yield forecasting and operational efficiency for growers.

Requirements:

• 5+ years of professional software engineering experience.

• Expert-level knowledge of Python, including modern language features, performance optimization, concurrency primitives (threading, multiprocessing), and best practices in production-grade code.

• Advanced mastery of relational database internals, specifically Postgre

SQL. Candidates must demonstrate proficiency in query optimization, relational algebra, and the distinct challenges of time-series data storage.

• Proven hands-on experience designing, building, and operating systems that process and aggregate large datasets, with expertise in distributed data processing frameworks and efficient aggregation pipelines.

• Deep understanding of algorithms and data structures, with the ability to analyze time/space complexity, select optimal solutions for real-world problems, and implement efficient algorithmic logic.

• Proven track record of designing, implementing, and product ionizing high-performance algorithms that operate reliably at scale in distributed environments.

• Solid theoretical and practical knowledge of graph theory, including traversal algorithms (DFS, BFS), shortest-path algorithms, topological sorting, cycle detection, centrality measures, and experience applying graph algorithms to real systems (e.g., dependency resolution, social networks, recommendation engines, or knowledge graphs).

• Comprehensive understanding of data pipeline dynamics, including scheduling strategies, event-driven vs. time-based triggering, deterministic execution guarantees, idempotency, exactly-once/late-data handling, and back pressure management.

• Hands-on experience with production orchestration platforms such as Kubernetes (including operators, CRDs, and Helm), Argo Workflows, Airflow, Prefect, Dagster, Temporal, or equivalent frameworks, with emphasis on reliability, observability, and scaling of complex workflows.

• Strong grasp of OS-level concurrency mechanisms (mutexes, semaphores, condition variables, read-write locks, atomic operations)…