Principal Program Manager, Databricks & Enterprise Data Platforms

Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets. An ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work® Institute and recognized as a ‘Cool Vendor’ and a ‘Vendor to Watch’ by Gartner.

Please visit Fractal | Intelligence for Imagination for more information about Fractal

Note: This position is not eligible for Immigration Sponsorship at this time

Location: New Jersey (Client onsite) 

Role Overview:

Fractal is seeking a Principal Program Manager to lead large-scale data, AI, and technology transformation initiatives for a leading global biopharmaceutical company based in the New Jersey region.

This is a senior, client-facing architecture leadership role for someone who can operate at the intersection of life sciences consulting, enterprise data platforms, AI/ML enablement, and cloud-native engineering. The person will partner with senior client stakeholders, data product owners, engineering leaders, and business teams to define modernization roadmaps, establish scalable architecture patterns, and guide the delivery of enterprise AI and data platforms on AWS and Databricks.

The ideal candidate is not just a strong technologist. They are a trusted advisor who can frame ambiguous business problems, shape solution strategy, lead executive-level conversations, and help clients modernize their data and AI foundations in a secure, scalable, governed, and cost-effective way.

Key Responsibilities

Program Manager

• Manage multiple concurrent Databricks initiatives for a leading global biopharmaceutical company, driving delivery across internal stakeholders and vendor partners.

• Be the Clients’ Trusted Advisor

• Drive senior client workshops focused on problem framing, solution strategy, modernization roadmaps, and AI/data platform transformation.

• Serve as a trusted technical advisor to VP, Executive Director, and senior business/technology stakeholders.

• Translate business priorities and analytical needs into scalable architecture strategies, data models, platform designs, and delivery roadmaps.

• Partner with client stakeholders to identify new opportunities where AI, analytics, data engineering, and cloud platforms can drive measurable business impact.

• Help drive client success by ensuring solutions are aligned to business outcomes, enterprise standards, and long-term scalability.

Enterprise AI & Data Architecture

• Own the architecture vision for modern life sciences data and AI platforms across complex, multi-team programs.

• Define reference architectures, reusable patterns, governance models, and engineering standards for AI-ready data platforms.

• Design scalable data architectures across ingestion, transformation, modeling, metadata, lineage, quality, consumption, and observability layers.

• Guide architecture decisions across Databricks, distributed compute, data engineering, analytics, and AI/ML enablement.

• Evaluate current-state data ecosystems and define practical future-state modernization roadmaps.

• Ensure platform designs are secure, reliable, observable, cost-efficient, and aligned with enterprise architecture best practices.

AI Foundations in Life Sciences

• Lead data and platform modernization efforts that support AI foundations, governance, operational layers, context layers, and ontology-driven architectures.

• Apply life sciences domain context to platform and architecture decisions, including experience with pharma data ecosystems, modernization, migration, and analytical workloads.

• Establish standards for data modeling, metadata management, lineage, data quality, governance, and operational excellence.

• Partner with business and technical teams to design architectures that support advanced analytics, AI/ML, self-service insights, and enterprise data products.

• Ensure solutions are built to support reliability, observability,