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Pune, Maharashtra, India
Hybrid
At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections, where you are valued, accepted and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters.
Diagnostics R&D at Roche generates data across modalities that few organisations encounter at the same scale: genomic sequences from Nanopore and NGS instruments, imaging data from digital pathology and clinical diagnostics, clinical trial data from global studies, and telemetry from laboratory instruments deployed worldwide. We are looking for a Data and Solution Architect who can design the architecture that connects these worlds, making data findable, interoperable, AI-ready, and compliant across a hybrid cloud and on-premises environment spanning eight countries.
The role is responsible for co-creating Diagnostics R&D data architecture strategy and establishing standards for metadata, interoperability, lineage, and AI-ready data models. It will enable scalable analytics and AI/ML while driving modernization of the landscape in partnership with engineering, business, and governance teams.
Description of the area
Job Responsibilities
Co-create scalable, secure, compliant, AI-enabled, interoperable and cost-effective cloud and data solutions tailored to the business area needs and aligned to the enterprise direction. Manages non-functional requirements.
Accountability/Problem Solving: Solves complex problems by taking a new perspective on existing solutions and exercising judgment based on the analysis of multiple information sources. Maintains the vision, roadmap, and current landscape for the solution. Assesses and consults on the impacts and dependencies of initiatives on capabilities, risk, and the application landscape. Creates Conceptual & Logical Data Models, Taxonomies, and Ontologies for the solution and its interfaces. Defines and drives reference architectures, patterns, and ensures Architecture Decision Records (ADRs) are appropriately recorded.
Impact/Strategy: Develops and maintains the product's technology roadmap and participates in architecture reviews. Provides technical oversight, guidance, and consultation on specific technologies for product teams. Improves existing solutions, manages technical debt, and researches, evaluates, pilots, and drives the adoption of new technologies. Identifies opportunities to enhance value and business processes.
Collaborate with business and engineering teams to design robust instrument-to-platform pipelines or edge-to-cloud patterns, transformation, context enrichment, storage, and access patterns optimized for AI/ML and advanced analytics. Build a common understanding of the business domain among tech lead and development teams
Partner with Solution Architects across the organizations in strategy, planning, definition, high-level design of the solution, and exploration of solution alternatives. Understand technical opportunities to improve the business process.
Facilitate technical and architectural discussions among tech leads and architects inside the Product Area and the Function.
Qualifications
Education / Experience
Bachelor's or Master’s degree in Computer Science, Bioinformatics, Data Science or related field.
Demonstrated experience designing business-driven, standards-based, future-ready solutions within a specific solution context.
Experience maintaining the vision, roadmap, and current landscape for technical solutions, and assessing the impacts of initiatives on risk and application landscapes.
Technical Skills
Proven track record acting simultaneously as a Solution Architect (system integration, workflow design, infrastructure) and a Data Architect (data modeling, governance). Proficiency in creating conceptual and logical data models, taxonomies and ontologies.
Cloud Expertise: 5+ years experience and deep knowledge of cloud infrastructure (AWS preferred, Azure, or GCP) with strong expertise in HPC setups and containerization (Docker, Kubernetes). DevOps practices, infrastructure-as-code.
Architecting scalable AI/ML infrastructure: Deep understanding of MLOps platforms (MLflow, SageMaker, Kubeflow), model registries, inference infrastructure and architecture for reproducible compute. Integration with enterprise AI/GenAI capabilities.
Design and recommend event-driven and streaming architectures (e.g. Kafka, AWS Kinesis) to support real-time data pipelines, agentic AI workflows and continuous monitoring requirements at scale.
Modern Data Paradigms: Hands-on architectural experience with distributed data systems, Data Mesh, Data Lakes, and Data Warehouse architectures (e.g., Snowflake, Databricks, AWS Lake Formation).
Experience in data modelling methodologies, metadata management, master/reference data, data lineage and enterprise governance frameworks.
Proficiency in designing observability, monitoring, data quality and operational resilience frameworks across distributed ecosystems.
FinOps principles and cloud cost governance: Embedded into all architecture designs, including cost-benefit analysis, right-sizing recommendations, and continuous cost optimization monitoring as core deliverables.
Experience working in agile teams (e.g. SAFe/Scrum)
Strong analytical, logical problem-resolution skills.
Experience in working in a multicultural and international environment.
Strong collaboration and influencing skills with a proven ability to lead and inspire.
Experience supporting globally distributed teams and driving alignment across multiple organizations, platforms, and strategic initiatives.
Additional Qualifications
Strong communication and stakeholder management skills, with the ability to explain difficult or sensitive information and successfully build consensus.
Proactive mindset to identify opportunities to enhance value and business processes, improve existing solutions, and manage technical debt.
Scientific Tooling Familiarity: Understanding of the software and data structures common to Diagnostics R&D, such as LIMS, ELN, DICOM, fastq/bam file variants, and clinical trial data formats is a big plus. Understanding of interoperability standards (DICOM, HL7 FHIR and others) and Allotrope Data Framework (ADF).
Experience within highly regulated environments (HIPAA/GDPR/POPIA, GxP, ALCOA+, FAIR principles) would be considered a strong plus.
A healthier future drives us to innovate. Together, more than 100’000 employees across the globe are dedicated to advance science, ensuring everyone has access to healthcare today and for generations to come. Our efforts result in more than 26 million people treated with our medicines and over 30 billion tests conducted using our Diagnostics products. We empower each other to explore new possibilities, foster creativity, and keep our ambitions high, so we can deliver life-changing healthcare solutions that make a global impact.
Let’s build a healthier future, together.
Roche is an Equal Opportunity Employer.