Senior Manager, Clinical Data Enablement

By clicking the “Apply” button, I understand that my employment application process with Takeda will commence and that the information I provide in my application will be processed in line with Takeda’s Privacy Notice and Terms of Use.  I further attest that all information I submit in my employment application is true to the best of my knowledge.

Job Description

Are you looking for a patient-focused, innovation-driven company to inspire you and empower you to shine? Join us as a Senior Manager, Clinical Data Enablement based remotely reporting to the Director, Clinical Data Engineering.

At Takeda, we are transforming the pharmaceutical industry through our R&D-driven market leadership and being a values-led company. To do this, we empower our people to work towards their potential through life-changing work. Certified as a Global Top Employer, we offer stimulating careers, and work toward excellence in everything we do. We foster an inclusive, collaborative workplace, in which our global teams are united by an unwavering commitment to provide Better Health and a Brighter Future to people around the world.

Here, you will be a necessary contributor to our inspiring, bold mission.

GOALS:

The Senior Manager, Clinical Data Enablement is responsible for operating and continuously improving end-to-end clinical data pipelines across cloud-based data lake and data warehouse environments. This role builds scalable ingestion and transformation capabilities, partners closely with internal stakeholders and external vendors, and ensures reliable, compliant delivery of clinical data products to downstream consumers.

ACCOUNTABILITIES:                                                                                 

• Own end-to-end clinical data pipelines across lake/warehouse environments (build, run, monitor, maintain, troubleshoot) to ensure stable, performant, traceable operations.
• Drive operational excellence through monitoring/alerting, incident management, root-cause analysis, and corrective/preventive actions.
• Build and scale cloud ETL/ELT capabilities (AWS/Azure) for efficient ingestion, transformation, orchestration, and delivery.
• Automate integrations and run operations (scheduling, metadata-driven pipelines, automated quality checks) to improve reliability and timeliness.
• Partner with vendors and cross-functional teams to define data transfer specifications, acceptance criteria, and integration patterns; implement integrations with robust error handling and reconciliation.
• Serve as an SME for structured and unstructured data integration, driving continuous improvements through stakeholder feedback and evolving requirements.
• Partner with downstream teams (Stat Programming, SDTM, Analytics, Clinical Data Programming) to deliver fit-for-purpose datasets and interfaces (format, structure, cadence).
• Ensure on-time, high-quality delivery to agreed specifications; establish and manage SLAs/OLAs as needed.
• Design and deliver GxP-aligned clinical data engineering solutions with lifecycle documentation, traceability, and audit-ready evidence.
• Embed data integrity controls (e.g., ALCOA+) and support validated operations via change control, incident/deviation documentation, periodic review, and inspection readiness.
• Collaborate with Quality/Compliance/IT to implement appropriate access controls, logging/audit trails (as applicable), and operating controls.

EDUCATION AND EXPERIENCE:

• BS/BA (or equivalent) plus 6+ years in data engineering, pipeline development/operations, and multi-source data integration (clinical/biopharma preferred).
• Strong SQL, data modeling, and data quality controls; working knowledge of NoSQL and integration patterns.
• Proficiency in Python (preferred) or R; experience building production pipelines (testing, logging, monitoring); familiarity with SDLC practices; Jira/Confluence required.
• Knowledge of CDISC, CDASH, and SDTM standards preferred. 
• Hands-on with CSV, JSON, XML, and API/file-based integrations.
• Cloud experience (e.g., AWS such as EC2/EMR/RDS/Redshift/Apache Spark) and related platforms (e.g., Snowflake, Databricks). Pipeline and platform expertise: reusable ELT/ETL frameworks; data warehouse/data lake technologies (e.g., Snowflake, Amazon Redshift); large-scale pipeline per