A BRIEF OVERVIEW

The Bioinformatics Engineer III position is a 3rd stage position for those with high levels of experience in the support of robust computational environments focused on the management, transfer, formatting, and general processing of complex data types associated with genomics and proteomics. Whereas the Bioinformatics Scientist is primarily focused on the outcome of an analysis in terms of the scientific endpoint, the Bioinformatics Engineer is primarily focused on the outcome of an analysis in terms of validity, efficiency, and robustness. The focus of the position is systems design for and implementation of data manipulation and analysis pipelines, applications, general hardware configurations, and bioinformatics-specific hardware configurations. The Bioinformatics Engineer III is also responsible for actively participating in the development of enterprise-wide systems by promoting the needs of large-scale bioinformatics and optimizing those systems in terms of the enterprise solution for sustainability. The position allows for a wide variety of activities described within the job responsibilities and encompasses positions within both research and clinical environments. Activities occur with a low degree of supervision with high latitude for independent judgment. The Bioinformatics Engineer III will be well versed in application of best practices as a bioinformatics engineer expert. The Bioinformatics Engineer III is primarily focused on supporting bioinformatics analytical groups and individuals as an engineering domain expert and strategic data resource.
The Bioinformatics Engineer III must be able to perform all responsibilities listed in the Bioinformatics Scientist I and Bioinformatics Engineer II job descriptions. All responsibilities listed in this description are additional.

EDUCATION QUALIFICATIONS

• Bachelor’s Degree - Required
• Master’s Degree computational discipline or systems engineering - Preferred

EXPERIENCE QUALIFICATIONS

• At least seven (7) years experience in a production clinical or research bioinformatics data processing role - Required
• At least eight (8) years in a production clinical or research bioinformatics data processing role -Preferred

SKILLS AND ABILITIES

• Extensive knowledge with high performance and parallel computing environments and data processing workflows essential.
• Extensive experience with data storage frameworks essential.
• Extensive knowledge of CPU- and IO-intensive bioinformatics data analysis applications.
• Demonstrated track record of optimizing systems to meet changing performance and load requirements.
• Extensive knowledge of HPC systems, job management applications, including methods for profiling performance, benchmarking, and optimizing multiple job types and scenarios in bioinformatics data processing.
• Ability to independently plan and execute pipelines and workflows of high complexity required.
• Ability to independently engineer systems relative to larger enterprise framework required.
• Strong UNIX/LINUX expertise required.
• Expertise in support mechanisms for applications written in common bioinformatics languages such as R, Python, Perl or similar required
• Expertise in support mechanisms for common bioinformatics applications, data sources, and data formats required.
• Knowledge of common microarray, NGS, mass spectrometry, or other high-throughput data formats is required.
• Expertise with resources of genomic data sets and analysis tools, such as UCSC Genome Browser, Bioconductor, ENCODE, and NCBI databases is required.
• Demonstrated ability to develop and implement best practices for bioinformatics systems integration, testing, and deployment is required.
• Expert knowledge with cloud computing concepts and applications is required.
• Ability to lead discussions with various information systems and technology owners to achieve desired bioinformatics outcomes is required.
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• Computational Environment Development and Optimization (60%):
• Integrate large-scale bioinformatics pipelines into larger sustainable enterprise data processing solutions.
• Integrate large-scale bioinformatics high performance computing (HPC) capability into larger sustainable enterprise analytical solutions.
• Integrate large-scale bioinformatics storage frameworks into larger sustainable enterprise storage solutions.
• Lead the development and evolution of a robust heterogeneous UNIX, LINUX, OSX environment.
• Evaluate trends in bioinformatics data production and analysis and identify likely requirements for bioinformatics data processing and storage systems. Communicate value of trends/innovations to organization using metrics and forecasting.
• Implement best practices for bioinformatics processing, storage, and manipulation of bioinformatics data
• Monitor new bioinformatics technologies, assess relevance to research or clinical systems, and contribute to benchmarking and evaluation.
• Act as primary contact for vendor-purchased systems and services and participates in negotiation process (or leads under supervision).
• Develop and implement a program for continuous assessment of commercial and open-source bioinformatics data processing solutions by applying structured benchmark evaluation.
• Collaboration and Mentorship (20%): Establish role within collaborative project team as primary computational engineering resource
• Lead projects where needed by serving as engineering point, especially for solutions involving coordination of multiple disciplines.
• Serve as leader for complete computational solution by assessing all stakeholders, including bioinformatics management, bioinformatics scientists, Digital & Technology Services staff, and principal investigators
• Proactively mentor lower tier engineering individuals and groups as needed
• Promote sustainability and enterprise adoption by maintaining successful key relationships with multiple stakeholders inside and outside the department.
• Documentation and Standardization (20%):
• Under supervision, lead the development of a formal bioinformatics engineering plan and development roadmap.
• Establishes policies and standards for data quality, completeness, and reproducibility.
• Establishes policies and standards for performance benchmarking and system stability.
• Develops and directs the execution of a robust data quality program, focused on transparency and reproducibility of results.
• Leads preparation and successful response program for audit and adherence to applicable regulatory requirements (e.g., HIPAA, CAP, CLIA).
• Promote sustainability and enterprise adoption through proactive documentation.