Enterprise Solution Architect (Life Sciences & Data Modernization)
Role Overview
We are seeking a seasoned Enterprise Solution Architect with deep expertise in the Life Sciences value chain and data modernization to lead complex, high-value client engagements. You will serve as a strategic bridge between business objectives and technical execution, driving architectural governance, solution shaping, and technical sales enablement, specifically within the Life Sciences sector.
Key Responsibilities
- Life Sciences Domain Leadership: Drive end-to-end data modernization across the Life Sciences value chain-spanning R&D, Clinical Data, and Commercial operations. Lead discovery workshops to define high-impact use cases that accelerate drug development and commercial outcomes.
- Solution Strategy & Shaping: Translate complex business problems into scalable, value-led architecture roadmaps across AWS, Azure, and GCP. Lead end-to-end solutioning for high-value pursuits.
- Architecture Leadership: Provide technical governance for critical programs. Drive the adoption of modern data platforms, including Lakehouse architectures for large-scale clinical and commercial data sets.
- Innovation & GenAI: Spearhead the development of GenAI assets (eg, agentic workflows for R&D/IT) and Proof-of-Concepts (POCs) tailored to Life Sciences workflows.
- Stakeholder Management: Partner with C-suite and senior stakeholders to align technical roadmaps with enterprise goals, ensuring compliance and data reusability across global research and commercial organizations.
- Technical Enablement: Establish best practices for the Life Sciences Center of Excellence (COE), mentor technical teams, and perform rigorous code reviews.
Qualifications & Expertise
- Experience: 20+ years of experience across architecture, application development, and big data analytics, with significant tenure in the Life Sciences industry.
- Domain Expertise: Proven track record in Life Sciences, specifically in Clinical Data, R&D, and Commercial Analytics. Deep understanding of the data challenges inherent in the Life Sciences value chain.
- Core Architecture: Expertise in Data Modernization, Microservices, Cloud Architecture, and Lakehouse patterns.
- Data & AI: Deep hands-on proficiency in Databricks (Spark, PySpark, DLT), GenAI (Agent Core, Copilot Studio, Amazon Bedrock), and Graph Databases (Neo4j).
- Cloud & Tools: Strong command of AWS (Lambda, Glue, EMR, MWAA, S3), Azure, Airflow, Terraform, Docker, Kubernetes, and Jenkins.
- Programming: Advanced skills in Python, SQL, Scala, Java, and Shell Scripting.
Leadership Competencies
- Strategic Advisory: Proven ability to lead post-technical win strategies and define long-term platform adoption roadmaps for enterprise-level Life Sciences programs.
- Delivery Ownership: End-to-end accountability for complex architectural solutions, ensuring successful production go-lives in regulated environments.
- Technical Enthusiasm: A continuous learner, proactive in navigating new product innovations and bridging the gap between Life Sciences business priorities and engineering feasibility.