Python Data Engineer

Fetcherr is an AI-driven company specializing in deep learning, algorithmic trading, and large-scale data solutions. Our core technology, the Large Market Model (LMM), enables accurate demand forecasting and real-time, data-driven decision-making. Originally focused on the airline industry, Fetcherr is expanding its AI solutions across additional industries.

Fetcherr is looking for a highly skilled and experienced Senior Python Developer to spearhead the development of robust infrastructure and services that power our data platform and data-driven products.

You will be instrumental in building scalable, reliable, and efficient systems that enable data engineers and analysts to ingest, process, and serve large-scale data for production use. This role requires a deep understanding of Python, cloud technologies, and a passion for building foundational backend and data infrastructure. If you are a seasoned developer with a proven track record in backend and data engineering, and you’re excited about building core data systems at Fetcherr, we want to hear from you.

Responsibilities:

• Design, build, and maintain scalable, high-performance backend services and infrastructure for Fetcherr’s data platform.
• Develop and manage APIs and microservices that support data pipelines, internal services, and end-user applications.
• Implement, optimize, and maintain data pipelines for ingesting, processing, transforming, and serving large volumes of data.
• Ensure the reliability, security, and efficiency of data processing and deployment environments through robust infrastructure management.
• Collaborate with Data Engineers, Analytics, and Product teams to understand data infrastructure needs and deliver scalable solutions.
• Establish and maintain best practices for Python development, including coding standards, testing (unit, integration, E2E), reproducibility, and version control.
• Leverage cloud platforms (e.g., GCP) to build and manage scalable infrastructure, including compute, storage, and networking resources.
• Implement monitoring, logging, and alerting to ensure the health, performance, and reliability of data services and pipelines.
• Contribute to architectural decisions and the long-term technical direction of Fetcherr’s data infrastructure.
• Stay up to date with industry trends and best practices in data engineering, cloud computing, and backend architecture.

Requirements

• 5+ years of professional experience in backend software development, with a strong emphasis on Python.
• Proven experience building and operating production-grade systems and scalable data infrastructure.
• Strong understanding of designing applications and data services that scale and operate reliably in cloud environments.
• Expertise in designing and implementing APIs and microservices supporting data platforms and data workflows.
• Solid experience with cloud platforms, particularly GCP (preferred), including compute, storage, networking, and managed data services.
• Demonstrated experience with best coding practices, including testing, reproducibility, and version control (e.g., Git).
• Familiarity with containerization technologies such as Docker and orchestration platforms like Kubernetes.
• Experience with CI/CD pipelines and tools for automated testing and deployment.
• Proficiency in database technologies, both SQL and NoSQL, including data modeling and performance optimization.
• Excellent problem-solving, analytical, and debugging skills.
• Strong communication and collaboration skills, with the ability to clearly articulate technical concepts.

Nice to have:

• Hands-on experience with data engineering platforms and frameworks (e.g., Apache Spark, Beam, Airflow).
• Experience building and maintaining large-scale data pipelines and ETL/ELT workflows.
• Experience with data streaming technologies (e.g., Kafka, Pub/Sub).
• Understanding of data reliability, data quality, and observability practices.
• Familiarity with infrastructure-as-code tools (e.g., Terraform, CloudFormation).
• Knowledge of security best practices for cloud-based data platforms and applications.