Sr Data Engineer

What you will do About the role You will play a key role as part of Operations Generative AI (GenAI) Product team to deliver cutting edge innovative GEN AI solutions across various Process Development functions(Drug Substance, Drug Product, Attribute Sciences & Combination Products) in Operations functions. Role Description: The Sr Data Engineer for GEN AI solutions across various Process Development functions(Drug Substance, Drug Product, Attribute Sciences & Combination Products) in Operations functions is responsible for designing, building, maintaining, analyzing, and interpreting data to provide actionable insights that drive business decisions, working with large datasets, developing reports, supporting and implementing data governance initiatives and visualizing data to ensure data is accessible, reliable, and efficiently managed. The ideal candidate has strong technical skills, experience with big data technologies, and a deep understanding of data architecture and ETL processes. Roles & Responsibilities: Design, develop, and maintain data solutions for data generation, collection, and processing. Be a key team member that assists in design and development of the data pipeline. Create data pipelines and ensure data quality by implementing ETL processes to migrate and deploy data across systems. Contribute to the design, development, and implementation of data pipelines, ETL/ELT processes, and data integration solutions. Take ownership of data pipeline projects from inception to deployment, manage scope, timelines, and risks. Collaborate with multi-functional teams to understand data requirements and design solutions that meet business needs. Develop and maintain data models, data dictionaries, and other documentation to ensure data accuracy and consistency. Implement data security and privacy measures to protect sensitive data. Leverage cloud platforms (AWS preferred) to build scalable and efficient data solutions. Develop solutions for handling unstructured data in AI pipelines. Collaborate with Data Architects, Business SMEs, and Data Scientists to design and develop end-to-end data pipelines to meet fast paced business needs across geographic regions. Identify and resolve complex data-related challenges. Adhere to standard processes 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. Collaborate and communicate effectively with product teams. What we expect of you We are all different, yet we all use our unique contributions to serve patients. Basic Qualifications: Masters degree with 4 - 6 years of experience in Computer Science, IT or related field OR Bachelors degree with 6 - 8 years of experience in Computer Science, IT or related field OR Diploma with 10 - 12 years of experience in Computer Science, IT or related field. Must-Have Skills: Hands on experience with big data technologies and platforms, such as Databricks, Apache Spark (PySpark, SparkSQL), workflow orchestration, performance tuning on big data processing. Proficiency in data analysis tools (eg. SQL) and experience with data visualization tools. Experienced with software engineering best-practices, including but not limited to version control (Git, Subversion, etc.), CI/CD (Jenkins, Maven etc.), automated unit testing, and DevOps Excellent problem-solving skills and the ability to work with large, complex datasets. Strong understanding of data governance frameworks, tools, and standard methodologies. Experience in implementing Retrieval-Augmented Generation (RAG) pipelines, integrating retrieval mechanisms with language models. Strong programming skills in Python and familiarity with deep learning frameworks such as PyTorch or TensorFlow. Experience in processing and leveraging unstructured data for GenAI applications Preferred Qualifications: Experience with ETL tools such as Apache Spark, and various Python packages related to data processing, machine learning model development. Strong understanding of data modeling, data warehousing, and data integration concepts. Knowledge of Python/R, Databricks. Knowledge of vector databases, including implementation and optimization. Professional Certifications: Certified Data Engineer / Data Analyst (preferred on Databricks or cloud environments). Machine Learning Certification (preferred on Databricks or Cloud environments). SAFe for Teams certification (preferred). Soft Skills: Excellent analytical and troubleshooting skills. Strong verbal and written communication skills Ability to work effectively with global, virtual teams High degree of initiative and self-motivation. Ability to manage multiple priorities successfully. Team-oriented, with a focus on achieving team goals