Drive innovation in the Life Sciences sector by architecting and implementing advanced data platforms. This role focuses on creating scalable, governed, and compliant data solutions to power analytics, reporting, and digital transformation across R&D, Clinical, Regulatory, and Commercial functions.

Join a dynamic team dedicated to leveraging enterprise technology as a catalyst for functional excellence and sustainable growth. This is an opportunity to shape the future of data in a critical industry.

Assess and rationalize existing data ecosystems, proposing target-state architectures and transformation roadmaps. Evaluate and recommend cutting-edge data platforms and tools, including data lakes, lakehouses, ETL/ELT solutions, and cataloging systems. Design end-to-end data lake architectures, establishing robust patterns and standards for enterprise data platforms.

Define comprehensive enterprise data strategies encompassing sourcing, storage, access, and lifecycle management. Implement robust data governance frameworks covering quality, lineage, cataloging, and master data management. Design data ingestion frameworks for diverse sources and enable interoperability across enterprise systems.

Spearhead cloud-based data lake and lakehouse implementations on AWS, Azure, or GCP. Modernize legacy platforms and optimize data performance, storage, and cost. Enable downstream analytics, AI/ML use cases, and business consumption through curated data marts and semantic layers. Ensure strict adherence to Life Sciences regulatory standards like GxP and 21 CFR Part 11, implementing strong data security measures.

Collaborate effectively with vendors and stakeholders, presenting solutions and driving capability building through best practices and knowledge sharing.

A minimum of 5 years of experience in data architecture or data engineering is essential, with a strong emphasis on Data Lake architecture and implementation. Deep domain expertise within the Life Sciences, Pharma, or Biotech sectors is required.

Proficiency in data modeling, ETL/ELT pipelines, and big data processing frameworks is crucial. Hands-on experience with cloud data platforms such as AWS S3 and Azure Data Lake is necessary. Familiarity with data governance, cataloging, lineage tools, and both structured and unstructured data processing is expected.

Solid understanding of GxP and regulatory data compliance requirements within the Life Sciences industry is a must. Experience with tools like Databricks, Snowflake, or Azure Data Factory, exposure to AI/ML use cases, data lakehouse architecture, and relevant cloud certifications are considered advantageous.