Python Data Developer

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

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Responsibilitie**

• s:Design, build, and maintain scalable, high-performance backend services and infrastructure for Fetcherr's data platfor
• m.Develop and manage APIs and microservices that support data pipelines, internal services, and end-user application
• s.Implement, optimize, and maintain data pipelines for ingesting, processing, transforming, and serving large volumes of dat
• a.Ensure the reliability, security, and efficiency of data processing and deployment environments through robust infrastructure managemen
• t.Collaborate with Data Engineers, Analytics, and Product teams to understand data infrastructure needs and deliver scalable solution
• s.Establish and maintain best practices for Python development, including coding standards, testing (unit, integration, E2E), reproducibility, and version contro
• l.Leverage cloud platforms (e.g., GCP) to build and manage scalable infrastructure, including compute, storage, and networking resource
• s.Implement monitoring, logging, and alerting to ensure the health, performance, and reliability of data services and pipeline
• s.Contribute to architectural decisions and the long-term technical direction of Fetcherr's data infrastructur
• e.Stay up to date with industry trends and best practices in data engineering, cloud computing, and backend architectur

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Requiremen**

• ts:5+ years of professional experience in backend software development, with a strong emphasis on Pyth
• on.Proven experience building and operating production-grade systems and scalable data infrastructu
• re.Strong understanding of designing applications and data services that scale and operate reliably in cloud environmen
• ts.Expertise in designing and implementing APIs and microservices supporting data platforms and data workflo
• ws.Solid experience with cloud platforms, particularly GCP (preferred), including compute, storage, networking, and managed data servic
• es.Demonstrated experience with best coding practices, including testing, reproducibility, and version control (e.g., Gi
• t).Familiarity with containerization technologies such as Docker and orchestration platforms like Kubernet
• es.Experience with CI/CD pipelines and tools for automated testing and deployme
• nt.Proficiency in database technologies, both SQL and NoSQL, including data modeling and performance optimizati
• on.Excellent problem-solving, analytical, and debugging skil
• ls.Strong communication and collaboration skills, with the ability to clearly articulate technical concep

ts.Nice to ha

• ve:Hands-on experience with data engineering platforms and frameworks (e.g., Apache Spark, Beam, Airflo
• w).Experience building and maintaining large-scale data pipelines and ETL/ELT workflo
• ws.Experience with data streaming technologies (e.g., Kafka, Pub/Su
• b).Understanding of data reliability, data quality, and observability practic
• es.Familiarity with infrastructure-as-code tools (e.g., Terraform, CloudFormatio
• n).Knowledge of security best practices for cloud-based data platforms and applicatio

ns.