Principal Program Manager, Databricks & Enterprise Data Platforms

Fractal Analytics•New York, NY

6d•Onsite

About The Position

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.

Requirements

12+ years of experience in data architecture, software engineering, cloud platforms, AI/ML platforms, data engineering, or enterprise solution architecture.
7+ years of experience in consulting, client advisory, or complex enterprise technology transformation.
Strong experience working with life sciences, pharmaceutical, healthcare, or regulated enterprise data environments.
Proven ability to lead architecture across large, multi-team programs with business, technology, and executive stakeholders.
Experience developing modernization roadmaps, future-state architecture models, platform strategies, and technical governance frameworks.
Executive presence with the ability to independently lead senior stakeholder conversations, steering committee discussions, and solution strategy sessions.
Strong hands-on architecture experience with Databricks, AWS, Spark, SQL, Python, and modern data engineering practices.
Experience with cloud-native data platforms, distributed compute, MPP systems, lakehouse architectures, and enterprise-scale analytical workloads.
Strong understanding of data modeling, metadata, lineage, data quality, governance, and platform observability.
Experience with CI/CD, DevOps, automated testing, monitoring, logging, and cost optimization practices.
Familiarity with dbt Core/Cloud, Data Vault 2.0, data product architecture, and modern data transformation patterns.
Experience designing architectures across ingestion, transformation, modeling, semantic/context layers, and consumption layers.
Experience with life sciences data ecosystems, pharma data modernization, migration, and governance.
Understanding of AI foundation concepts including operational layers, context/ontology layers, data readiness, governance, and scalable platform enablement.
Ability to connect life sciences business problems to practical AI, analytics, and data platform solutions.
Experience driving adoption of enterprise data platforms, modern data practices, and AI-ready engineering standards.

Nice To Haves

Experience advising Fortune 500 or Fortune 50 clients in life sciences, healthcare, pharma, or regulated industries.
Experience with AWS services such as S3, Glue, Redshift, EMR, Lambda, Athena, Kinesis, DynamoDB, or related cloud-native services.
Experience with Databricks platform architecture, Lakehouse patterns, Unity Catalog, ML/AI enablement, or large-scale migration programs.
Experience contributing to thought leadership, solution accelerators, reusable architecture patterns, or client-facing transformation offerings.
Experience working in a global delivery model with onsite/offshore engineering and architecture teams.

Responsibilities

Manage multiple concurrent Databricks initiatives for a leading global biopharmaceutical company, driving delivery across internal stakeholders and vendor partners.
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.
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.
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, automation, and long-term adoption.
Provide architecture leadership across onsite/offshore teams, engineering squads, data product teams, and client stakeholders.
Review solution designs for scalability, performance, security, maintainability, and cost optimization.
Guide technical execution across complex programs without becoming a bottleneck for delivery teams.
Establish CI/CD, DevOps, testing, monitoring, and operational practices that support enterprise-grade platform delivery.
Mentor architects, engineers, and technical leads on modern data platform design and AI-ready architecture patterns.
Drive technical visioning, thought leadership, in-person workshops, and client-facing architecture discussions.