Description:

• As a Data Analyst in AbbVie’s Genomics Research Center’s Bioinformatics Engineering team, you will be responsible for developing and running workflows to standardize and load project datasets in a centralized environment.
• You will work closely with Bioinformatics Engineering, as well as therapeutic area facing bioinformatics research scientists to leverage common data models, and support GRC Bioinformaticians’ needs for loading and querying.
• Your expertise in PostgreSQL for database management and Python and R for scripting and automation will be crucial in developing and maintaining ETL processes to ensure data quality and integrity.

Responsibilities

• Develop and maintain a functional understanding of the GRC common data models, loading processes, and requirements, and perform accurate and efficient loading of new and historical datasets into the GRC’s Omics Data Server.
• Collaborate with Bioinformatics Engineers to develop and implement additional data loading workflows.
• Partner with Bioinformatics research scientists to identify, process, and load project data into the common data models.
• Build and execute ETL processes to integrate non-GRC generated high-value datasets into the common data models.
• Keep thorough documentation for tracking datasets and loading tasks.
• Ensure reproducibility and facilitate collaboration with team members by documenting and versioning code with git.

Qualifications

• Bachelor's degree in computer science, bioinformatics, or a related field +3 years of experience.
• Experience with building and running workflows for RDMS data loading and ETL processes.
• Proficient in PostgreSQL (or equivalent) and ability to write complex queries for data extraction and analysis.
• Strong programming skills in Python for scripting and automation. Additional experience with R is preferred.
• Familiarity with genomic data formats and databases commonly used in bioinformatics research.
• Knowledge of data modeling concepts and implementing common data models in a relational database.
• Familiarity with data cleaning, normalization, and quality control processes.
• Excellent communication skills and ability to collaborate with researchers and stakeholders.