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Data Engineer Leader for AI and Data Science Projects
1 month ago
GSK is a leading global pharmaceutical company that prioritizes advancing healthcare to improve humanity's well-being. Our mission focuses on researching, developing, and delivering innovative medicines and vaccines. To achieve this, we rely heavily on data-driven research and employ AI/ML and data science to uncover new insights, enhance analytics, and streamline processes.
Job OverviewThis role is part of a team working on projects involving AI/ML, generative AI, information retrieval, and data science. The team's future projects will cover diverse areas like regulatory, clinical, legal, and HR. As a versatile professional, you will be able to quickly grasp domain data and requirements, translating them into effective solutions. You will collaborate with architects, software and data engineers, modelers, data scientists, AI/ML engineers, product owners, and other Clinical Solutions and R&D team members.
About the TeamThe Data Engineering team is responsible for designing, delivering, supporting, and maintaining industrialized automated end-to-end data services and pipelines. They apply standardized data models and mapping to ensure data accessibility for users through APIs. Additionally, they define best practices and ensure compliance with Quality Management principles and alignment to automated data governance.
Main Responsibilities- Developing a detailed specification for functions, pipelines, services, or components, and delivering them at a high level.
- Providing technical leadership and guidance to junior data engineers.
- Awareness and adherence to best practices in software development, including code quality, documentation, DevOps practices, and testing.
- Familiarity with common tools in the data space, such as Spark, Databricks, Kafka, ADF/AirFlow, Snowflake, Denodo, etc.
To succeed in this position, you should have:
- A Bachelor's degree in data engineering, computer science, software engineering, or a related field.
- Experience in industry as a data engineer.
- Knowledge of choosing appropriate data structures for scale and access patterns.
- Familiarity with at least one common programming language, preferably Python, along with toolchains for documentation and testing.
- Exposure to modern software development tools and ways of working, such as git/GitHub and DevOps tools.
- Software engineering experience.
- Familiarity with databases and SQL.
$140,000 - $180,000 per year, depending on location and experience.
