About the RoleWe're looking for a Data Software Engineer to help build and scale the data infrastructure that powers our proprietary Large Market Model. You'll design and maintain the pipelines that ingest, process, and serve massive volumes of real-time market information and data, giving our pricing, inventory, and network optimization engine the fuel they need to make decisions in real time. This is a high-impact role at the intersection of data engineering and applied AI, working directly with data scientists, ML engineers, and product teams.
What You'll Do- Design, build, and maintain scalable, low-latency data pipelines that ingest live market information and data from diverse internal and external sources.
- Develop robust ETL/ELT processes to clean, transform, and validate large-scale, high-velocity datasets feeding our proprietary Large Market Model.
- Architect and optimize data storage solutions (data lakes, warehouses, streaming systems) for both real-time and batch workloads.
- Partner closely with data scientists and ML engineers to productionize models and ensure data is delivered in the right shape, at the right time, at scale.
- Monitor, troubleshoot, and continuously improve the performance, reliability, and cost-efficiency of production data systems.
- Implement data quality, observability, and governance practices across the pipeline lifecycle.
- Contribute to the evolution of our data platform architecture as Fetcherr scales across new airline customers and product lines.
Requirements- 5+ years of full-time experience as a Data Engineer, Software Engineer, or similar role focused on large-scale data systems that are in production. Lab, proof of concept, prototype, academic, and part-time experiences do not apply to this experience.
- Strong programming skills in Python, with production-grade coding practices.
- Strong SQL skills and experience with, relational and non-relational data stores.
- Experience and comfort with AI-assisted software development, but also be able to write code yourself
- Experience with cloud infrastructure (GCP BigQuery preferred) and modern data warehouse & data lakehouse technologies.
- Familiarity with orchestration tools (Airflow & Dagster) and CI/CD practices for data pipelines.
- Experience operating high-throughput, low-latency systems in production.
- Strong analytical mindset, attention to data quality, and a pragmatic, get-it-done engineering approach.
- Excellent collaboration and communication skills, comfortable working cross-functionally with data science and product teams.
Nice to Have- Experience working with real-time OLAP engines such as ClickHouse
- Experience in travel, e-commerce, logistics, or other dynamic-pricing/real-time-decisioning domains
- Experience in Software-As-A-Service environment
- Exposure to machine learning pipelines or MLOps practices.
