Senior Data Engineer

Data Science and Analytics is an ambitious team of Data Scientists and Data Engineers with a unique blend of Business, Scientific, and Machine Learning expertise that seeks to unleash the full potential of AbbVie’s data assets by bringing technology and/or data and insights to the forefront of decision making via fit-for-purpose solutions.

The Data Engineer role in DSA is a unique role with blend of data engineering responsibilities in the context of Data Science/ML and AI. A key role to enable application of advanced analytics techniques across clinical development processes. Drives analytics innovation and experimentation to enable data-driven insights for complex business problems across R&D by deploying advanced analytics techniques like Machine Learning and Visual Analytics.

Responsibilities:

• Collaborates with the Data Scientists and cross-functional data stewards to identify appropriate data sources and to enable streamlined data flow for the Data Science and Analytics capabilities across clinical development.
• Develop, construct, test and maintain architectures (such as databases and large-scale process systems) to support strategic projects within Clinical Development
• Build data products and processes which perform data transformation, metadata extraction, workload management and error processing and management.
• Implement standardized, automated operational and quality control processes to deliver accurate and timely data and reporting.
• Adhere to best practices for coding, testing and designing reusable code/component.
• Contribute to the discovery and understanding of new tools, techniques and technologies to change course as needed.
• Develop solutions to improve data reliability, efficiency and quality.
• Ensures adherence to federal regulations and applicable local regulations, Good Clinical Practices (GCPs), ICH Guidelines, AbbVie Standard Operating Procedures (SOPs), and to functional quality standards. Stays abreast of new and/or evolving local regulations, guidelines and policies related to clinical development.
• Self-starter, comfortable with ambiguity, experience initiating and driving projects with minimal oversight and guidance.
• Establish and follow data science and engineering best practices including peer review, code review, documentation, coding standards, data quality and ensure reproducibility and compliance.
• Highly autonomous and productive in accomplishing the deliverables with minimal direction.

Tools and skills you will use in this role:

• Advanced SQL and Python/R
• Working with databases like Oracle, Microsoft SQL server etc.
• Data Cataloging platform like Alation etc.
• Cloud platform like AWS and/or Azure, including but not limited to services like S3, EC2, EMR, MWAA, etc.
• ETL, Orchestration and scheduling tools like Informatica and Airflow
• JIRA, Confluence and agile project methodology
• Interaction with BI tools like PowerBI, Tableau and Spotfire, Web app platforms like Plotly Dash, RShiny etc.

Requirements

• MS or PhD in computer science or other quantitative disciplines with 3-5 years of experience.
• 3+ years of hands-on experience in advanced SQL, Python, or R.
• Experience delivering Data and Analytical initiatives on modern cloud-based data warehouse solutions, cloud computing and cloud operations preferably AWS and/or Azure.
• Experience with ETL, scheduling and orchestration tools like Airflow is preferred.
• End-to-end experience with data, including querying, aggregation, analysis, and visualization; Fluency in both structured and unstructured data is preferred.
• Experience with IaaS(Infrastructure as Code) and general understanding of end-to-end solution architecture for data science and advanced analytics solutions is preferred.
• Experience with large scale data processing and distributed computing using Spark/Scala is a plus.
• Experience with containerization, DevOps methodologies, Docker/Kubernetes ecosystem is a plus.
• Experience in Data Science and Advanced Analytics domain is preferred.
• Familiarity and general understanding of Machine Learning and statistical models is preferred.
• Biotech / Pharma experience is a plus.
• Good communication and presentation skills, being able to explain complex problems and the solutions applied, and comfortable in presenting technical solutions to a nontechnical audience.
• Demonstrated history of successful execution in a fast-paced environment and in managing multiple priorities effectively.