Tiger Analytics is pioneering what AI and analytics can do to solve some of the toughest problems faced by organizations globally. We develop bespoke solutions powered by data and technology for several Fortune 100 companies. We have offices in multiple cities across the US, UK, India, and Singapore, and a substantial remote global workforce.
We are seeking a highly skilled and experienced Data Engineering Manager with expertise in the life science industry. As the Data Engineering Manager, you will lead a team of data engineers responsible for designing, developing, and maintaining robust data infrastructure and pipelines to support our life science initiatives. You will collaborate closely with cross-functional teams, including data scientists, researchers, and business stakeholders, to ensure the efficient and accurate collection, storage, and analysis of large-scale life science data sets. This role requires a deep understanding of life science data domains, strong leadership abilities, and technical proficiency in data engineering technologies.
- Lead and manage a team of data engineers, providing mentorship, guidance, and performance evaluations.
- Design, develop, and maintain scalable and efficient data pipelines, ensuring the smooth flow of data from various sources to downstream applications.
- Collaborate with cross-functional teams to understand life science data requirements, provide data engineering expertise, and drive the development of innovative solutions.
- Ensure data quality and integrity by implementing data governance and validation processes.
- Stay up-to-date with emerging technologies and trends in the life science industry, recommending and implementing relevant tools and frameworks to enhance data engineering capabilities.
- Optimize data storage and retrieval processes, considering performance, scalability, and cost-efficiency.
- Collaborate with IT infrastructure teams to ensure robust data security, privacy, and compliance with regulatory requirements.
- Lead the evaluation and selection of data engineering tools and platforms, considering the specific needs of life science data.
- Drive the adoption of best practices in data engineering, including data modeling, ETL (Extract, Transform, Load) processes, and data warehousing techniques.
- Act as a technical point of contact for stakeholders, providing insights and recommendations on data engineering strategies and solutions.