Life Sciences AI & Databricks Data Architect Lead

Bestica Inc.

Princeton (NJ)

On-site

USD 140,000 - 190,000

Full time

6 days ago

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Job summary

Bestica Inc. seeks a Technical Life Sciences Consultant to drive AI-led business transformation for a leading pharmaceutical client in Princeton, NJ. The role blends life sciences consulting with data architecture, Databricks, AWS, and AI/data modernization.

You will collaborate with business and technology leaders to design scalable, secure data and AI platforms. Ideal candidates bring 10+ years in AI/engineering, 5+ years pharma consulting, and hands-on Databricks, Python, Spark, and SQL

Qualifications

10+ years of experience in AI, software development, data engineering, data architecture, or related fields.
5+ years of life sciences/pharmaceutical consulting experience, preferably with commercial pharma.
Strong experience with Databricks and modern data platform architecture.
Strong hands-on knowledge of Python, Spark, and SQL.
Experience with AWS data and cloud services.
Strong understanding of distributed computing, cloud-native architectures, and modern data engineering patterns.
Experience with data modeling and Data Vault 2.0; exposure to automate_dv is a plus.
Experience designing and implementing scalable data pipelines and analytical platforms.
Understanding of data governance, metadata, lineage, data quality, and security.
Experience with CI/CD, DevOps, testing, monitoring, and observability.
Strong communication and stakeholder management skills.
Ability to operate effectively across multi-team onsite/offshore delivery models.

Responsibilities

Lead client workshops focused on problem framing, solution design, and technical strategy.
Partner with business and technology stakeholders to translate requirements into scalable data and AI architectures.
Provide end-to-end technical advisory across complex transformation programs.
Present architecture recommendations and technical roadmaps to senior client stakeholders.
Support RFP/RFI responses, solution proposals, and technical evaluations.
Identify opportunities for modernization, platform adoption, and new AI/data capabilities.
Design and guide implementation of modern, cloud-native data platforms using AWS and Databricks.
Develop scalable architectures supporting analytical, operational, and AI/ML workloads.
Define data models, data pipelines, consumption layers, and platform patterns.
Translate complex analytical requirements into scalable data architectures.
Assess current-state environments and define future-state data and AI platform architectures.
Establish architectural standards and reusable patterns for scalability, performance, security, and cost optimization.
Architect solutions using Databricks, Apache Spark, Python, and SQL.
Work with distributed compute and modern data processing paradigms.
Design data ingestion, transformation, orchestration, and consumption frameworks.
Apply modern data modeling approaches, including Data Vault 2.0 where appropriate.
Collaborate with engineering teams on implementation, optimization, and technical execution.
Help establish foundational capabilities required for enterprise AI adoption.
Define standards for metadata, lineage, data quality, data modeling, and governance.
Support operational, context, and ontology layers within modern AI/data platforms.
Establish frameworks for observability, monitoring, reliability, and operational excellence.
Drive adoption of enterprise data platforms and modern data engineering practices.