Data Analyst

You will be joining the Data Analytics and AI (DA&AI) organization within Operations IT, responsible for delivering state-of-the-art Data Analytics and AI solutions for Operations capability areas like Pharmaceutical Technology Development, Manufacturing & Global Engineering, Quality Control, Sustainability, Supply Chain, Logistics and Global External Sourcing and Procurement. Here our work has a direct impact on patients – transforming our ability to develop life-changing medicines. We empower the business to perform at its peak and lead a new way of working, combining cutting-edge science with leading digital technology platforms and data.

As the data engineer, you will be responsible for designing, building, and maintaining the infrastructure and systems necessary to collect, store, process, and analyze large volumes of data. They play a critical role in ensuring that data is accessible, reliable, and ready for analysis by data scientists, analysts, and other stakeholders. You will work closely with technical leads and cross-functional teams to implement data solutions that meet business needs.
Key Responsibilities: Develop, Maintain, and Optimize the scalable data pipelines for data integration, transformation, and analysis, ensuring high performance and reliability. Demonstrate proficiency in ETL/ELT processes, including writing, testing, and maintaining high-quality code for data ingestion and transformation. Improve the efficiency and performance of data pipeline and workflows, utilizing advanced data engineering techniques and best practices. Troubleshoot and resolve complex issues related to data pipelines and applications, using logs and code reviews to ensure smooth data flow and system functionality. Work closely with data scientists, analysts, and stakeholders to understand requirements and deliver effective, high-quality datasets and data solutions. Utilize and manage cloud-based services, particularly on AWS, for data storage and processing, ensuring optimal use of resources. Ensure data accuracy and integrity by implementing data validation and cleansing techniques to maintain consistency. Perform unit testing, system integration testing, regression testing, and assist with user acceptance testing to ensure data solutions meet quality standards.