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:

Soft Skills:

Mgr Data Engineer - Research Data and Analytics