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Job Description
Clinical Data Sciences (CDS) at Takeda:
Key to Takeda’s success the Clinical Data Sciences team provides strategic planning, integrating, execution, build and oversight of clinical trial deliverables. The Clinical Data Sciences group is responsible for integrating structured and unstructured data across the various data sources, setup, data transfer/review and support downstream transformation and analysis.
The Clinical Data Sciences comprises of the Clinical Data Engineering and Clinical Data Standards. While the Clinical Data Standards provides the standards for clinical operation and data flow, the Clinical Data Engineering team drives the data architecture for clinical data. CDS also provides support to exploratory and specialty data for the purposes of data modelling, simulation, and analysis.
Clinical Data Engineer (CDE), Clinical Data Sciences (CDS):
Key to Takeda’s success is the Clinical Data Engineering team, provides strategic planning, integrating, execution, build and oversight of clinical trial deliverables. CDE leads the integration, design, development, and execution of data pipelines for the ingestion of clinical data from all sources at an enterprise level for use by the clinical data configuration specialist at the study level. The CDE is an enterprise level role and is primarily responsible for ensuring smooth end to end processes for data collection/ingestion from all data collection sources, providing an output into a data lake that is fit for use by downstream end users. The CDE is also responsible for developing and tracking KPIs and other measures across the business and providing continuous improvement for both process and tools. The CDE will also develop and maintain libraries, tools, and reports to increase reuse and overall efficiency for study level roles. The CDE should have a strong understanding of end-to-end clinical data collection and extraction processes as well as strong project management and technical experience. The CDE will be working with cross functional stakeholders to ensure alignment on processes and requirements and often will be required to convert these requirements to technical specifications. The CDE may also need to develop tools and visualizations as part of the continuous improvement process.
Under the guidance of Clinical Data Sciences, the Clinical Data Engineer provides leadership and guidance at the enterprise level for end-to-end data extraction, transformations and construct of data pipelines that conform to the common data model that ensures data ingestion for all clinical data capture technologies and other related vendor and/or applications (e.g., EDC, IRT, ePRO, eCOA) as well other data models that may be required by end users. Understands and ensures proper data formats for all downstream users for use in the data lake. Facilitates test data transfer to staging instance and confirms accurate DTA specification. Defines processes and develop and maintain code libraries for use by clinical data configuration specialist to build, maintain, and monitor data pipelines for clinical data and the clinical data repository (CDR) alongside processing specialty data for exploratory analysis.
Develops and maintains library of reusable mapping and transformation functions to be used across studies. CDE contributes to the successful conduct of Takeda’s clinical trials and to the delivery of high quality in a timely manner, which is eventually used for statistical analysis and submitted to regulatory authorities for the approval of Takeda products. The CDE also monitors end to end performance and KPIs and provides continuous improvement to processes and tools. Further, CDE efforts enable valid secondary use of clinical trial data throughout Takeda research groups to maximize value and achieve company objectives.
Key Accountabilities:
- Experience building data pipelines for various heterogenous data sources
- Identifying, designing, and implementing scalable data delivery pipelines and automating manual processes
- Building required infrastructure for optimal data extraction, transformation and loading of data using cloud technologies like AWS, Azure etc.
- Develop end to end processes on the enterprise level for use by the clinical data configuration specialist to prepare data extraction and transformations of raw data quickly and efficiently from various sources at the study level
- Coordinate with downstream users such as statistical programmers, SDTM programming, analytics, and clinical data programmers to ensure that outputs meet requirements of end users
- Experience creating ELT and ETL to ingest data into data warehouse and data lakes
- Experience creating reusable data pipelines for heterogenous data ingestions
- Manage and maintain pipelines and troubleshoot data in data lake or warehouse
- Provide visualization and analysis of data stored in data lake
- Define and track KPIs and provide continuous improvement
- Develop and maintain, tools, libraries, and reusable templates of data pipelines and standards for study level consumption by data configuration specialist
- Collaborate with various vendors and cross functional teams to build and align on data transfer specification and ensure a streamlined process of data integration
- Provide ad-hoc analysis and visualization as needed
- Ensure accurate delivery of data format and data frequency with quality deliverables per specification
- Participate in the development, maintenance and training rendered by standards and other functions on transfer specs and best practices used by business.
- Collaborate with system architecture team in designing and developing data pipelines as per business needs
- Network with key business stakeholders on refining and enhancing the integration of structured and non-structured data
- Provide expertise for structured and non-structured data ingestion
- Develop organizational knowledge of key data sources, systems and be a valuable resource to people in the company on how to best integrate data to pursue company objectives.
- Provides technical leadership on various aspects of clinical data flow including assisting with the definition, build, and validation of application program interfaces (APIs), data streams, data staging to various systems for data extraction and integration
- Experience in creating data integrity and data quality checks for data ingestion
- Coordinates with data base builders, clinical data configuration specialists and data management (DM) programmers ensuring accuracy of data integration per SOPs
- Provide technical support / consultancy and end-user support, work with Information Technology (IT) in troubleshooting, reporting, and resolving system issues
- Develop and deliver training programs to internal and external team, ensure timely communication of new and/or revised data transfer specs
- Continuous Improvement/Continuous Development
- Efficiently prepare and process large datasets for various end users for downstream consumption
- Understand end to end requirements for stakeholders and contribute to process and conventions for clinical data ingestion and data transfer agreements
- Adhere to SOPs for computer system validation and all GCP (Good Clinical Practice) regulations
- Ensure compliance with own Learning Curricula, corporate and/or GxP requirements
- Assists with quality review of above activities performed by a vendor, as needed
- Assess and enable clinical data visualization software in the data flows
- Performs other duties as assigned within timelines
- Performs clinical data engineering tasks according to applicable SOPs (standard operating procedures) and processes.
Educational Qualification:
- Bachelor’s degree in computer science, statistics, biostatistics, mathematics, biology or other health related field or equivalent experience that provides the skills and knowledge necessary to perform the job.
Experience:
- BS with 8+ years’ experience. Minimum of 5 years’ experience in data engineering, building data pipelines to manage heterogenous data ingestions or similar in data integration across multiple sources including collected data.
- Experience with Python/R, SQL, NoSQL
- Cloud experience (i.e. AWS, AZURE or GCP)
- Experience with GitLab, GitHub
- Experience with Jenkins, GitLab
- Experience deploying data pipelines in the cloud
- Experience with Apache Spark (databricks)
- Experience setting up and working with data warehouse, data lakes (eg: snowflake, Amazon RedShift etc.,)
- Experience setting up ELT and ETL
- Experience with unstructured data processing and transformation
- Experience developing and maintaining data pipelines for large amounts of data efficiently
- Must understand database concepts. Knowledge of XML, JSON, APIs.
- Demonstrated ability to lead projects and work groups. Strong project management skills. Proven ability to resolve problems independently and collaboratively.
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