Technical Life Sciences Consultant, Pharma (Business Translator) - New York New Jersey
AOB Recruitment Technical Life Sciences Consultant, Pharma (Business Translator) - New York New Jersey
Summary
Consulting role leading AI-driven data and cloud transformation programs for life sciences and pharma clients, architecting cloud-native data platforms and advising executive stakeholders. Core stack includes AWS, Databricks, dbt, Python, Spark, and SQL.
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 EngagementLead 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.
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.
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 Expertise10+ 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.
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.
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)