- Lead, manage, and mentor a high-performing team of data engineers
- Design, develop, and implement data pipelines, ETL processes, and data integration solutions
- Take ownership of data pipeline projects from inception to deployment, manage scope, timelines, and risks
- Develop and maintain data models for biopharma scientific data, data dictionaries, and other documentation to ensure data accuracy and consistency
- Optimize large datasets for query performance
- Collaborate with global multi-functional teams including research scientists to understand data requirements and design solutions that meet business needs
- Implement data security and privacy measures to protect sensitive data
- Leverage cloud platforms (AWS preferred) to build scalable and efficient data solutions
- Collaborate with Data Architects, Business SMEs, Software Engineers and Data Scientists to design and develop end-to-end data pipelines to meet fast paced business needs across geographic regions
- Identify and resolve data-related challenges
- Adhere to best practices for coding, testing, and designing reusable code/component
- Explore new tools and technologies that will help to improve ETL platform performance
- Participate in sprint planning meetings and provide estimations on technical implementation
Basic Qualifications:
- Doctorate Degree OR
- Masters degree with 4 - 6 years of experience in Computer Science, IT, Computational Chemistry, Computational Biology/Bioinformatics or related field OR
- Bachelors degree with 6 - 8 years of experience in Computer Science, IT, Computational Chemistry, Computational Biology/Bioinformatics or related field OR
- Diploma with 10 - 12 years of experience in Computer Science, IT, Computational Chemistry, Computational Biology/Bioinformatics or related field
Preferred Qualifications:
- 3+ years of experience in implementing and supporting biopharma scientific research data analytics (software platforms)
Functional Skills:
Must-Have Skills:
- Proficiency in SQL and Python for data engineering, test automation frameworks (pytest), and scripting tasks
- Hands on experience with big data technologies and platforms, such as Databricks, Apache Spark (PySpark, SparkSQL), workflow orchestration, performance tuning on big data processing
- Excellent problem-solving skills and the ability to work with large, complex datasets
- Able to engage with business collaborators and mentor team to develop data pipelines and data models
Good-to-Have Skills:
- A passion for tackling complex challenges in drug discovery with technology and data
- Good understanding of data modeling, data warehousing, and data integration concepts
- Good experience using RDBMS (e.g. Oracle, MySQL, SQL server, PostgreSQL)
- Knowledge of cloud data platforms (AWS preferred)
- Experience with data visualization tools (e.g. Dash, Plotly, Spotfire)
- Experience with diagramming and collaboration tools such as Miro, Lucidchart or similar tools for process mapping and brainstorming
- Experience writing and maintaining technical documentation in Confluence
- Understanding of data governance frameworks, tools, and best practices
Professional Certifications:
- Databricks Certified Data Engineer Professional preferred
Soft Skills:
- Excellent critical-thinking and problem-solving skills
- Good communication and collaboration skills
- Demonstrated awareness of how to function in a team setting
- Demonstrated presentation skills
