Clinical Data Engineer RDX (DQ)

The Clinical Data Engineer (Data Quality) role is responsible for ensuring the accuracy, completeness, consistency, and reliability of data within key systems. This role is critical for maintaining the integrity of clinical, portfolio management, and product submission data, enforcing data quality principles, and bolstering regulatory compliance efforts. The ideal candidate should possess strong knowledge of the role data quality plays in the data lifecycle and ensures that data inputs and outputs meet all specified Data Quality (DQ) criteria.

Through close collaboration with study leads, regulatory experts, and DigitalX professionals, this role establishes and upholds robust Data Quality standards that align with business objectives, regulatory mandates, and industry standards. Expertise in Data Quality, Data Governance, and adeptly managing governance processes is essential for maintaining the integrity of data assets. This role holds a pivotal position in advancing the life sciences company's mission by using data to drive the facilitation of data-driven decision-making processes, expediting drug discovery initiatives, and bolstering regulatory compliance efforts.

Responsibilities And Accountabilities

• Responsible for Data Quality business rules implementation, DQ Analysis, and DQ Remediation activities.
• DQ Checks Implementation:
• Participate as needed in the creation of data quality checks and standards for multiple source systems.
• Implement business data quality checks on the SaaS platform (E.g. Databricks), working in conjunction with business teams, and data lake teams.
• Team-up with Data Lake teams in orchestration of DQ checks code across the various environments, leading to a successful Production deployment.
• Experience with unit testing and test-driven development (TDD), Test Scripts Execution and Approval in Lifecycle Management.
• Perform required validation and testing, to ensure DQ output matches the rules’ criteria, ensuring data accuracy, integrity and consistency across systems and applications.
• Collaborate with external vendors and partners to optimize data quality services.
• DQ Support and Remediation:
• Identify, document and track DQ violations and defects, work with development, data lake teams to resolve issues.
• Define, monitor, and report on Data Quality metrics.
• Collaborate internally in the creation of repeatable processes and continuous improvements.
• Lead DQ investigations to analyse issues and determine business impact.
• Establish ongoing data quality monitoring to ensure DQ issues are quickly identified and resolved.
• Provide post-release support related to Data Quality problems, workflows, and DQ batch job runs.
  
  

Responsibilities

 Other Responsibilities:

• Assist and coordinate for data operations with data lake teams on creating, automating, and monitoring the data quality processes, ensuring continuous improvement.
• Assist in enforcing data governance and information management policies, SOPs, and standards to manage clinical and product data in a secure and compliant manner.
• Improving the delivery of complex DQ projects by implementing standard processes, tools, and templates that will be used consistently across the Division.
• Participate in process improvements initiatives involving business areas.
• Identify areas for improvement with current DQ tool sets and propose future state to support DigitalX use cases, analytics best practices, and regulations.
• Provide short range, tactical planning and execution of actions required to deliver DQ strategies and objectives.
• Implement as needed metadata standards, data quality and testing procedures for the data lake/warehouse consistent with Astellas SLC standards for GxP/non-GxP sources.
• Work closely with Business Users and Pod/Value Team leads in developing data health insights on Tableau, Qlik or equivalent dashboards.
• Monitoring the integration of data sources, such as clinical trials data, operational data, portfolio management data, and product submission data into the centralized data Lakehouse.
• Mentors analysts to perform a variety of ad-hoc analysis and reports as requested around data quality and integrity.
  
  

Required Qualifications

• Bachelor of Science degree in Computer Science, Mathematics, Information Systems, Data Science, Business, Finance or a related field.
• 3+ years of relevant experience working in data quality management, data governance, data analysis, or related roles within a healthcare industry.
• Data quality principles, healthcare, clinical and health plan data, and associated data quality business rules.
• Strong problem skills and detail oriented, regarding data accuracy and consistency.
• Experience with cloud-based platforms i.e. AWS, Azure, and Google Cloud Platform.
• Excellent written and verbal communication skills to implement business data quality checks and collaborating effectively across cross-functional teams.
• Strong understanding of ETL/ELT processes, with experience monitoring and validating data quality across the different stages of the data pipelines.
• Good understanding of the SLC processes for GxP and non-GxP data sets.
• Experienced in Agile Methodologies with ability to foster continuous incremental delivery.
  
  

Preferred Qualifications

• Masters degree in Computer Science, Business, or Information Management.
• At least 2+ years’ experience in Life Sciences industry.
• Demonstrated understanding of clinical trial processes.
• Proficiency in SQL and data quality tools (e.g. Talend, Databricks, Informatica Data Quality)
• Expertise in Data Analysis, data modelling, and data integration techniques.
• Knowledge in integration solutions platforms such as Talend, Databricks.
• Proficiency in usage of Tableau, or Qlik dashboards.
• Knowledge of Data Lakes, Data Lakehouse, and Data Mesh architectures would be beneficial.
• In-depth understanding of life sciences business processes, adept at translating business requirements into effective technical solutions.
• Innovative problem-solving abilities for addressing complex data-related challenges.
• Excellent communication and interpersonal skills, enabling effective collaboration with cross-functional teams, business stakeholders, and technical experts.
• Project management capabilities, ensuring adherence to timelines for successful solution delivery.
• Relevant certifications in cloud computing and data engineering tools/platforms are advantageous.
  
  

Category

Astellas is committed to equality of opportunity in all aspects of employment.

EOE including Disability/Protected Veterans