Principal Software Engineer – Microscopy Data Management & Cloud Platform in India at Jobgether

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Principal Software Engineer – Microscopy Data Management & Cloud Platform based in India.

This is a hands-on principal engineering role focused on building a distributed scientific data platform for microscopy and life-science applications.
You will design systems capable of managing large-scale image data, metadata, search, transformation, storage, transfer, and analysis workflows.
The platform will operate across laboratory instruments, user devices, on-premise environments, and AWS cloud infrastructure.
You’ll tackle complex challenges around high-throughput data pipelines, reliable transfers, database architecture, and distributed multi-user systems.
The role combines deep technical execution with architectural leadership, influencing engineering standards and cross-team technical direction.
You’ll collaborate closely with specialists across image analysis, instrument software, web applications, cloud engineering, and scientific domains.
This is a fully remote opportunity in India with significant scope to shape a platform supporting advanced scientific and life-science workflows.

• Design and develop a distributed data management platform supporting microscopy, scientific imaging, and life-science workflows.
• Architect scalable storage, indexing, search, caching, and high-throughput data-transfer mechanisms for large scientific datasets across hybrid edge, on-premise, and cloud environments.
• Design and implement robust backend APIs, contracts, schemas, and interfaces that enable interoperability between instrument control systems, image-analysis applications, user interfaces, and cloud services.
• Build reliable and resumable data-transfer pipelines between instruments, local systems, and cloud environments, including solutions for offline-first and intermittently connected scenarios.
• Develop high-performance data ingestion, streaming, and transformation pipelines for large microscopy datasets and associated metadata.
• Establish mechanisms for data integrity, consistency, traceability, versioning, reproducibility, checksums, validation, and auditability.
• Contribute to database architecture, data lifecycle management, query performance, indexing strategies, and scalable data-access patterns.
• Collaborate with image-analysis teams, web UI engineers, instrument software developers, and scientific domain experts to define robust interfaces and end-to-end workflows.
• Contribute to engineering standards covering CI/CD, observability, reliability, security, and cloud-based as well as instrument-hosted software delivery.
• Provide technical leadership through architectural decisions, hands-on implementation, design reviews, technical problem-solving, and cross-team alignment.
• Mentor engineers and help establish scalable engineering practices for complex distributed systems.
• Evaluate emerging technologies and approaches that can improve data discovery, scientific workflows, platform scalability, and system performance.

• Master’s degree in STEM or equivalent practical experience.
• 10+ years of software engineering experience, including significant experience designing complex distributed backend systems or data platforms.
• Strong hands-on programming skills in Python and C#.
• Deep expertise in database design, schema evolution, query optimization, transactions, indexing, data security, and data lifecycle management.
• Strong experience designing backend APIs using REST and/or gRPC, including contract versioning, backward compatibility, and interface governance.
• Solid understanding of network protocols and performance optimization, including HTTP/2, gRPC, TCP/IP behavior, latency, throughput, and network-performance trade-offs.
• Experience designing asynchronous and event-driven architectures using message queues, streaming systems, or comparable technologies.
• Proven understanding of data-integrity mechanisms such as checksums, hashing, validation, and consistency models.
• Strong experience designing AWS-based backend systems, cloud storage, compute infrastructure, and scalable services.
• Excellent architectural thinking, system decomposition, troubleshooting, and performance-optimization capabilities.
• Experience with scientific or imaging data systems, microscopy, digital imaging, laboratory software, or scientific data pipelines is highly desirable.
• Familiarity with microscopy metadata, OME concepts, scientific image formats, tiled or multiresolution imagery, and image-processing workflows is an advantage.
• Experience designing search and discovery solutions for complex metadata and large datasets is beneficial.
• Knowledge of secure multi-user systems, authentication, authorization, auditability, and identity platforms such as Keycloak or LDAP/Active Directory is a plus.
• Experience with S3-compatible object storage, hybrid deployments, and on-premise/cloud synchronization is desirable.
• Familiarity with Docker, Kubernetes, Terraform, C++, Bash, or PowerShell is advantageous.
• Exposure to vector search, AI/ML data retrieval patterns, life-science ontologies, scientific data standards, or regulated/quality-driven environments is a plus.
• Strong communication and collaboration skills, with the ability to influence technical direction across multiple engineering and scientific teams.

• Full-time, fully remote position based in India.
• Opportunity to work on advanced scientific data-management and microscopy platforms with real-world applications in life sciences.
• Significant technical ownership across distributed systems, cloud architecture, data engineering, and backend platform design.
• Opportunity to influence architecture, engineering standards, reliability, security, and platform strategy at a principal level.
• Collaboration with multidisciplinary teams spanning software engineering, cloud, scientific imaging, instrumentation, and data analysis.
• Exposure to AWS, large-scale scientific datasets, hybrid cloud/on-premise environments, and modern distributed-system technologies.
• Opportunities to mentor engineers, shape technical practices, and contribute to complex, high-impact engineering initiatives.