Technical Life Sciences Consultant, Pharma (Business Translator) - New York + New Jersey

Technical Life Sciences Consultant – AI, Data & Cloud Platforms

Location: New York / New Jersey

Note: This position is not eligible for immigration sponsorship at this time.

Position Summary

We are seeking a Technical Life Sciences Consultant to lead AI-driven business transformation initiatives for enterprise Life Sciences organizations using modern cloud-native data platforms built on AWS and Databricks. This role is responsible for end-to-end client engagement, technical consulting, program leadership, and solution delivery, ensuring scalable, secure, and cost-effective architectures aligned with modern data engineering, analytics, AI/ML, and governance best practices.

The successful candidate will partner with engineering teams, data product owners, architects, and business stakeholders to define enterprise architecture standards, design cloud-native data platforms, and guide technical execution across complex transformation programs.

Key Responsibilities

Client Advisory & Stakeholder Engagement

• Lead executive workshops focused on business problem definition, solution design, and strategic planning.

• Serve as a trusted advisor to senior business and technology stakeholders throughout the project lifecycle.

• Drive opportunity development, client engagement, and demand generation activities.

• Collaborate with business and technical teams to align technology solutions with organizational objectives.

• Support responses to RFPs, RFIs, solution assessments, and technical evaluations.

AI & Data Platform Architecture

• Design and implement enterprise AI and data platform architectures supporting Life Sciences use cases.

• Apply expertise in Life Sciences data domains, modernization initiatives, and enterprise AI foundations.

• Design architectures supporting operational data layers, semantic and ontology-based models, metadata management, and contextual data frameworks.

• Establish and enforce standards for data modeling, metadata, lineage, governance, and data quality.

• Implement CI/CD pipelines, automated deployment processes, and monitoring frameworks.

• Ensure solutions meet enterprise requirements for scalability, observability, reliability, security, and operational excellence.

Cloud & Data Engineering Leadership

• Architect end-to-end cloud-native solutions using AWS and Databricks.

• Define architectural standards, reusable design patterns, and engineering best practices.

• Review solution designs to ensure scalability, performance, security, and maintainability.

• Evaluate emerging technologies and recommend long-term architecture strategies.

• Translate business and analytical requirements into scalable logical and physical data models.

• Assess existing enterprise data architectures and develop future-state architecture roadmaps.

• Design data models supporting both operational and analytical workloads.

• Implement metadata management, data lineage, governance, and enterprise data quality frameworks.

• Apply distributed computing principles and cloud-native architecture patterns across large-scale environments.

Required Qualifications

Technical Expertise

• 10+ years of experience in AI, software engineering, data engineering, data architecture, or cloud platform development.

• 5+ years of consulting or solution architecture experience supporting Life Sciences, pharmaceutical, biotechnology, or healthcare organizations.

• Experience leading enterprise modernization initiatives involving AI, analytics, governance, operational data layers, and semantic data models.

• Strong expertise with Databricks, dbt (Core or Cloud), Python, Apache Spark, SQL, and modern distributed computing architectures, including in-memory and massively parallel processing (MPP) environments.

• Experience with Data Vault 2.0 methodologies, including automation frameworks such as automate_dv.

• Deep knowledge of AWS services, including Amazon S3, AWS Glue, Amazon Redshift, Amazon EMR, Amazon DynamoDB, AWS Lambda, Amazon Athena, and Amazon Kinesis.

Leadership & Consulting

• Experience leading technical architecture across multi-team delivery models, including distributed and global teams.

• Executive presence with the ability to lead discussions with senior business and technology stakeholders.

• Strong consulting, facilitation, presentation, and client relationship management skills.

• Experience driving technical strategy, architecture governance, workshops, and enterprise transformation initiatives.

Data & AI Foundations

• Ability to translate complex analytical and business requirements into scalable architectures, including data models, ETL/ELT pipelines, semantic layers, and data consumption frameworks.

• Experience designing and deploying enterprise reporting, dashboards, and self-service analytics across relational and non-relational data platforms.

• Strong understanding of CI/CD, DevOps practices, automated testing, static code analysis, and modern software engineering principles.

• Experience leading cloud migration initiatives and designing modern cloud-native data platforms.

• Expertise defining enterprise data standards, metadata models, lineage, governance, and data quality frameworks.

• Experience implementing logging, monitoring, observability, performance optimization, and cloud cost management.

• Demonstrated success driving enterprise adoption of modern data platforms, cloud architectures, and AI-enabled solutions.

Preferred Qualifications

• Experience working with Life Sciences, pharmaceutical, biotechnology, or healthcare data domains.

• Knowledge of ontology-driven architectures, semantic modeling, knowledge graphs, and contextual data frameworks.

• Experience implementing enterprise AI, Machine Learning, and Generative AI solutions within regulated industries.

• Familiarity with regulatory requirements, data governance, and compliance standards applicable to Life Sciences organizations.

• Experience mentoring technical teams and establishing enterprise architecture standards and best practices.

Success Measures

• Successful delivery of enterprise AI and cloud transformation initiatives.

• High levels of client satisfaction and executive stakeholder engagement.

• Development of scalable, secure, and governed cloud data platforms.

• Adoption of enterprise architecture standards and engineering best practices.

• Delivery of high-quality technical solutions that improve operational efficiency, analytics capabilities, and business outcomes.

• Effective collaboration across consulting, engineering, architecture, and business teams.

Must Haves

• 10+ years in AI, software development, data engineering, or data architecture 
• 5+ years in life-sciences consulting 
• Driven Modernization initiatives for AI products with AI Foundations, governance, operational layers, context layers 
• Strong experience with Databricks, dbt Core/Cloud, Python, Spark, SQL, distributed compute paradigms (in-memory, distributed, MPP), Data Vault 2.0 experience (including automate_dv) 
• Deep awareness with AWS services (S3, Glue, Redshift, EMR, DynamoDB, Lambda, Athena, Kinesis)