Technical Life Sciences Consultant – AI, AWS & Databricks
Umanist NA
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Job summary
Umanist NA is seeking a Technical Life Sciences Consultant to lead AI-driven business transformation and modern data platform initiatives for major Life Sciences and Pharmaceutical clients in the New York/New Jersey area.
The role blends hands-on technical architecture with client advisory work, requiring deep expertise in data modeling, AWS, Databricks, dbt, Python, Spark, SQL, and Data Vault 2.0, and the ability to collaborate with senior stakeholders across multi-disciplinary teams.
Qualifications
- 10+ years in AI, software development, data engineering, or related technical fields.
Responsibilities
- Lead architecture across multi-team onsite and offshore delivery models.
Skills
Tools
Job description
Location: New York, NY / New Jersey
Employment Type: Full-Time
Experience: 10–15 Years
We are seeking a Technical Life Sciences Consultant to lead AI-driven business transformation and modern data platform initiatives for major Life Sciences and Pharmaceutical clients.
The ideal candidate will bring strong experience in Life Sciences consulting, AI foundations, data architecture, AWS, Databricks, dbt, Python, Spark, SQL, and Data Vault 2.0.
This role combines hands-on technical architecture with client consulting, executive stakeholder management, solution design, and technical leadership across complex enterprise data and AI programs.
- Client & Stakeholder Leadership
- Lead senior client workshops, discovery sessions, problem framing, and solution framing.
- Partner with business, technical, engineering, and data teams to translate business requirements into scalable technical solutions.
- Lead client advisory engagements and support opportunity creation and demand generation.
- Support RFP/RFI responses, architecture assessments, and technology evaluations.
- Present technical strategies and recommendations to senior client stakeholders and executive leadership.
- Drive technical vision, thought leadership, and client success.
- Lead modernization initiatives for AI products and enterprise data platforms within the Life Sciences domain.
- Work with Life Sciences and pharmaceutical datasets, migration initiatives, and modernization programs.
- Design AI foundations incorporating data, governance, operational, ontology, and context layers.
- Define standards for data modeling, metadata, lineage, data quality, and governance.
- Translate complex analytical and business requirements into scalable data models, pipelines, and consumption layers.
- Support enterprise adoption of modern AI and data practices.
- Architect end-to-end cloud-native data and AI solutions using AWS and Databricks.
- Define reusable architecture patterns, standards, and best practices.
- Review technical designs for scalability, performance, security, reliability, and cost efficiency.
- Evaluate technology options and recommend long-term architectural strategies.
- Assess current-state data architectures and define future-state models.
- Design data platforms supporting both analytical and operational workloads.
- Work with Databricks, dbt Core/Cloud, Python, Spark, and SQL.
- Apply distributed computing paradigms including in-memory, distributed, and MPP architectures.
- Design scalable ETL/ELT pipelines and modern data platforms.
- Apply Data Vault 2.0, including automate_dv.
- Define data models and consumption layers for analytics and AI applications.
- Establish metadata, lineage, data quality, and governance frameworks.
- Drive enterprise data platform modernization and adoption.
Experience with several of the following AWS services is required:
- Amazon S3
- AWS Glue
- Amazon Redshift
- Amazon EMR
- Amazon DynamoDB
- AWS Lambda
- Amazon Athena
- Amazon Kinesis
- Design and support CI/CD pipelines for data and AI platforms.
- Apply DevOps, static code analysis, and test-driven development practices.
- Establish logging, monitoring, observability, and operational excellence frameworks.
- Support reliability, performance, security, and cost optimization initiatives.
- Implement cloud migration and modernization patterns.
- Lead architecture across multi-team onsite and offshore delivery models.
- Provide technical direction and architectural guidance to engineering teams.
- Lead technical workshops and solution visioning sessions.
- Mentor technical teams and promote modern data engineering practices.
- Independently lead meetings with VP and Executive Director-level stakeholders.
- Manage multiple priorities across complex enterprise programs.
Required Qualifications
- 10+ years of experience in AI, software development, data engineering, data architecture, or related technical fields.
- 5+ years of Life Sciences consulting experience.
- Proven experience driving AI product modernization and enterprise data platform transformation.
- Strong experience with Databricks.
- Strong experience with dbt Core or dbt Cloud.
- Strong hands-on experience with:
- Python
- Spark
- SQL
- Strong understanding of distributed computing, including:
- In-memory computing
- Distributed computing
- MPP architectures
- Strong experience with Data Vault 2.0.
- Experience with automate_dv is highly preferred.
- Strong AWS knowledge with services such as S3, Glue, Redshift, EMR, DynamoDB, Lambda, Athena, and Kinesis.
- Experience designing modern cloud-native data platforms.
- Experience with data migration and cloud modernization.
- Strong knowledge of data modeling, metadata, lineage, data quality, and governance.
- Experience with CI/CD and DevOps practices.
- Strong understanding of logging, monitoring, observability, and cost optimization.
- Excellent communication, consulting, problem-solving, and stakeholder management skills.
Skills: design,architecture,modernization,data,cloud,amazon,life sciences,aws,leadership,enterprise
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