Staff Software Development Engineer

Fortinet

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Staff Software Development Engineer

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Roles & Responsibilities:

Lead the architecture, design, development, and enhancement of scalable, reliable, and maintainable software products and platforms.

Design and develop efficient, reusable, testable, modular, and scalable applications and services.

Design solutions for data-intensive and distributed workloads, including data extraction, processing, transformation, cleaning, enrichment, and delivery.

Drive product enhancements and new feature development while ensuring alignment with product objectives and long-term technical strategy.

Apply appropriate design patterns, object-oriented principles, data structures, algorithms, and architectural practices to solve complex engineering problems.

Conduct code and design reviews, promoting high standards for quality, security, performance, reliability, and maintainability.

Mentor engineers through technical guidance, design discussions, code reviews, knowledge sharing, and problem solving.

Improve engineering processes, development practices, automation, and team productivity through continuous improvement.

Requirements:

9 to 12 years of relevant work experience in software engineering and back-end application development.

Proven track record of delivering production-grade systems.

Strong hands-on experience with Python and developing RESTful APIs and backend services using FastAPI or similar frameworks.

Strong experience designing and implementing micro-services and distributed systems.

Hands-on experience with containerized application deployment.

Experience with SQL databases, and NoSQL databases, such as MongoDB, Opensearch/Elasticsearch including indexing, mapping, search, querying, and performance optimization.

Experience with data extraction, web scraping, data ingestion, cleaning, transformation, processing, and enrichment.

Strong understanding of API design, service-oriented architectures, integrations, and asynchronous/distributed processing.

Excellent communication and collaboration skills with both technical and non-technical stakeholders.
Good to Have:

Experience applying AI and Machine Learning techniques to data-related problems.

Exposure to LLMs, Generative AI, embedding, vector search, classification, entity matching, and automated data enrichment.

Experience with large-scale data platforms, distributed data processing, event-driven architectures, or data pipelines.

Experience contributing to technical strategy, architecture, and engineering standards across multiple teams.