We are looking for an experienced Senior Clinical Data Engineer to design, build, and manage scalable data pipelines supporting clinical trials.
In this role, you will be responsible for the end-to-end ingestion, integration, transformation, and delivery of clinical data from multiple source systems into enterprise data platforms such as data lakes and data warehouses. You will work closely with clinical data teams, analytics teams, and other downstream consumers to ensure that clinical data is reliable, traceable, high quality, and ready for use.
Requirements
Bachelor's degree ( BE/BTech/BS/BA ) in Computer Science, Information Technology, Engineering, Life Sciences, Health Sciences, or a related discipline.
5+ years of experience in Data Engineering, Software Engineering, or a related technology role.
Strong hands-on programming experience with Python and SQL .
Hands-on experience with AWS cloud platform .
Experience designing and deploying production-grade ETL/ELT data pipelines in cloud environments.
Hands-on experience with distributed/big-data processing technologies such as Apache Spark .
Experience with workflow orchestration and scheduling using Apache Airflow .
Experience working with data lakes and/or cloud data warehouses , such as Snowflake or Amazon Redshift.
Strong understanding of database concepts, data modeling, data integration, and large-scale data processing.
Experience handling semi-structured data formats such as JSON and XML .
Experience building or consuming REST/API-based integrations .
Familiarity with source control and CI/CD tools such as GitLab/GitHub .
Strong understanding and practical application of the Software Development Life Cycle (SDLC) .
Clinical Domain Experience
Experience working with clinical trial data and clinical data workflows.
Familiarity with clinical systems and data sources such as EDC/eCRF, CTMS, and other clinical trial source systems .
Ability to understand study-level data requirements and translate them into scalable technical solutions.
Experience coordinating or supporting study-level data transfer and integration specifications is preferred.
Understanding of data quality, traceability, validation, and regulatory expectations within a clinical environment is highly desirable.
Key Competencies
Strong analytical and critical-thinking skills.
Excellent problem-solving and troubleshooting abilities.
Ability to independently own technical deliverables while working effectively within cross-functional teams.
Strong communication skills with the ability to collaborate with both technical and clinical stakeholders.
Ability to understand complex data requirements and translate them into practical engineering solutions.
Strong focus on data quality, reliability, scalability, and automation .
Ability to manage priorities and deliver within clinical study timelines.
Technology Landscape
Programming: Python (Advanced) Big Data: Apache Spark Orchestration: Apache Airflow Cloud: AWS Data Platforms: Amazon Redshift / Data Lakes Databases: SQL Data Integration: REST APIs, JSON, XML, file-based integrations DevOps: GitLab / GitHub, CI/CD Clinical Systems: EDC/eCRF, CTMS, clinical source systems
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