Purpose and Scope: As a Foundation Data Engineer, you will play a crucial role in designing, building, and maintaining our data infrastructure. Your expertise in Databricks, PySpark, SQL and other related technologies, will be instrumental in driving data-driven decision-making across the organization. You will play a pivotal role in building maintaining and enhancing our systems across the organization. This is a fantastic global opportunity to use your proven agile delivery skills across a diverse range of initiatives, utilize your development skills, and contribute to the continuous improvement/delivery of critical IT solutions. Essential Job Responsibilities: Data Pipeline Development: Design, build, and optimize data pipelines using Databricks and PySpark. Ensure data quality, reliability, and scalability. Application Transition: Support the migration of internal applications to Databricks (or equivalent) based solutions. Collaborate with application teams to ensure a seamless transition. Mentorship and Leadership: Lead and mentor junior data engineers. Share best practices, provide technical guidance, and foster a culture of continuous learning. Data Strategy Contribution: Contribute to the organizations data strategy by identifying opportunities for data-driven insights and improvements. Participate in smaller focused mission teams to deliver value driven solutions aligned to our global and bold move priority initiatives and beyond. Design, develop and implement robust and scalable data analytics using modern technologies. Collaborate with cross functional teams and practices across the organization including Commercial, Manufacturing, Medical, DataX, GrowthX and support other X (transformation) Hubs and Practices as appropriate, to understand user needs and translate them into technical solutions. Requirements Qualifications: Required Bachelor's degree in computer science, Information Technology, or related field (Masters preferred) or equivalent experience. 5+ years experience in data engineering with a strong understanding of PySpark and SQL, building data pipelines and optimization. 5+ years experience in data engineering and integration tools (e.g., Databricks, Change Data Capture) 5+ years experience of utilizing cloud platforms (AWS, Azure, GCP). A deeper understanding/certification of AWS and Azure is considered a plus. Experience with relational and non-relational databases. Any relevant cloud-based integration certification at professional level. For example: AWS certified DevOps engineer (Professional), AWS Certified Developer (Professional) DataBricks Certified Engineer Mulesoft Certified integration architect Level 1 (Essential), Microsoft Certified Azure Integration and Security. Proficient in RESTful APIs AWS, CDMP, MDM, DBA, SQL, SAP, TOGAF, API, CISSP, VCP (any relevant certification MuleSoft Understanding of MuleSoft's Anypoint Platform and its components Experience with designing and managing API-led connectivity solutions Knowledge of integration patterns and best practices Proficiency in network architecture and security concepts AWS Experience provisioning, operating, and managing AWS environments Experience developing code in at least one high-level programming language Understanding of modern development and operations processes and methodologies Ability to automate the deployment and configuration of infrastructure using AWS services and tools Experience with continuous integration and continuous delivery (CI/CD) methodologies and tools Microsoft Azure Fundamental understanding of Microsoft Azure and its services Experience with Azure services related to computing, networking, storage, and security Knowledge of general IT security principles and best practices Understanding of cloud integration patterns and Azure integration services such as Logic Apps, Service Bus, and API Management Preferred Subject Matter Expertise: possess a strong understanding of data architecture/ engineering/operations/ reporting within Life Sciences/ Pharma industry across Commercial, Manufacturing and Medical domains. Other complex and highly regulated industry experience will be considered across diverse areas like Commercial, Manufacturing and Medical. Data Analysis and Automation Skills: Proficient in identifying, standardizing, and automating critical reporting metrics and modelling tools Analytical Thinking: Demonstrated ability to lead ad hoc analyses, identify performance gaps, and foster a culture of continuous improvement. Technical Proficiency: Strong coding skills in SQL, R, and/or Python, coupled with expertise in machine learning techniques, statistical analysis, and data visualization. Agile Champion: Adherence to DevOps principles and a proven track record with CI/CD pipelines for continuous delivery. Working Environment At Astellas we recognize the importance of work/life balance, and we are proud to offer a hybrid working solution allowing time to connect with colleagues at the office with the flexibility to also work from home. We believe this will optimize the most productive work environment for all employees to succeed and deliver. Hybrid work from certain locations may be permitted in accordance with Astellas Responsible Flexibility Guidelines.
