
Advanced Biopharma Scientific Data Specialist
3 days ago
As a pivotal member of our team, you will be responsible for crafting and developing data lake solutions that inform business decisions for research purposes. You will create scalable and high-performance data engineering solutions for large scientific datasets and collaborate closely with our research partners to drive innovation.
- Leadership & Collaboration: Lead and manage high-performing teams of data engineers, fostering their growth and ensuring adherence to best practices. Engage with business stakeholders to develop data pipelines and models.
- Data Pipeline Development: Design, develop, and implement data pipelines, ETL processes, and data integration solutions. Take ownership of data pipeline projects from inception to deployment, managing scope, timelines, and risks.
- Data Modeling & Optimization: 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.
- Collaboration & Solution Design: Collaborate with cross-functional teams, including researchers, data architects, business SMEs, software engineers, and data scientists, to understand data requirements and design end-to-end solutions that meet fast-paced business needs.
- Security & Compliance: Implement data security and privacy measures to protect sensitive data.
- Innovation & Process Improvement: Explore new tools and technologies to improve ETL platform performance. Identify and resolve data-related challenges and adhere to best practices for coding, testing, and designing reusable components.
- 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 and Apache Spark (PySpark, SparkSQL).
- Expertise in workflow orchestration and performance tuning on big data processing.
- Good understanding of data modeling, data warehousing, and data integration concepts.
- Knowledge of cloud data platforms (AWS preferred).
- Experience using RDBMS (e.g., Oracle, MySQL, SQL Server, PostgreSQL) is a plus.
- Experience with data visualization tools (e.g., Dash, Plotly, Spotfire) is a plus.
- Experience with diagramming and collaboration tools such as Miro or Lucidchart is a plus.
- Understanding of data governance frameworks, tools, and best practices is a plus.
- Databricks Certified Data Engineer Professional certification is preferred.
- A Doctorate Degree, or a Master's degree with relevant experience, or a Bachelor's degree with extensive experience, or a Diploma with a substantial background in Computer Science, IT, Computational Chemistry, Computational Biology/Bioinformatics, or a related field.
- Experience in implementing and supporting biopharma scientific research data analytics (software platforms) is preferred.
- Problem-Solving: Excellent critical-thinking and problem-solving skills, with the ability to work with large, complex datasets.
- Communication: Good communication and collaboration skills, with demonstrated presentation abilities.
- Leadership: Ability to engage with business stakeholders and mentor teams to develop data pipelines and data models.
- Proactiveness: A passion for tackling complex challenges in drug discovery with technology and data.
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