Senior Manager Data Engineering Manufacturing Data & Analytics Applications
What you will do In this vital role you will responsible for building, managing, and mentoring an impactful team of data scientists and data engineers operating within agile product teams across Amgens global manufacturing network. The successful candidate will be accountable for developing and maintaining advanced analytics applications, data pipelines, and operational frameworks that drive business value, data integrity, and operational efficiency throughout our manufacturing processes.
The ideal candidate is deeply familiar with manufacturing data and its diverse use cases, and has hands-on expertise with Databricks and AWS technologies. They possess a strong background in software engineering, data management, and cloud architectures, and are passionate about fostering innovative solutions in a highly regulated, fast-paced environment.
Key Responsibilities -
Team Leadership: Manage and mentor a team of data scientists and data engineers, fostering a culture of technical excellence, collaboration, innovation, and continuous improvement.
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Agile Product Delivery: Oversee multiple agile product teams developing and maintaining manufacturing data and analytics applications used at Amgens global manufacturing sites. Ensure seamless integration of agile ways of working, and drive delivery of high-quality, scalable solutions.
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Data Pipeline Development: Lead design, implementation, and optimization of data pipelines enabling ingestion, validation, transformation, and publishing of data across cloud and on-premises systems, with a focus on Databricks and AWS.
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Manufacturing Data Expertise: Ensure solutions are tailored to manufacturing data use cases, including process optimization, predictive maintenance, quality analytics, and regulatory compliance.
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Collaboration & Collaborator Engagement: Partner with Digital, Technology & Innovation, Operations, Quality, IT, Automation, and Data Science leaders to align operational priorities and ensure solutions meet evolving business needs.
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Governance & Compliance: Drive adherence to data quality, privacy, GxP regulations, and audit readiness. Collaborate with governance, compliance, and security teams to maintain regulatory alignment and standard methodologies.
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Technology Strategy: Evaluate, adopt, and guide the lifecycle management of emerging technologies (Databricks, AWS, data lakes, AI/ML, containerization) to enhance analytics capabilities for advanced manufacturing use cases.
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Incident Management & Root Cause Analysis: Serve as the partner concern point for data-related and application incidents, ensuring timely resolution, root cause analysis, and continuous improvement.
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Vendor & External Partnerships: Manage external partnerships, budget, and resources to support strategic objectives and drive optimal performance of data platforms and analytics solutions.
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Documentation & Knowledge Sharing: Oversee comprehensive documentation, training, and knowledge management programs to support team development and global adoption of manufacturing data applications.
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Change Management: Champion change management strategies to embed data-driven thinking, analytics adoption, and agile methodologies at all organizational levels.
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Representation: Represent Amgens Manufacturing Data Engineering strategy, practices, and results to senior leadership, regulatory agencies, and external technology partners as required.
What we expect of you We are all different, yet we all use our unique contributions to serve patients.
Basic Qualifications:
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Doctorate degree / Master's degree / Bachelor's degree and 12 to 17 years Engineering, Computer Science, Data Science, Information Systems, or a related field.
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Minimum 8 years of experience in data engineering, analytics applications, and solution architecture, with demonstrable expertise in manufacturing data environments.
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Extensive experience architecting and delivering solutions using Databricks and AWS technology stacks for manufacturing data use cases.
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Proven leadership of global, multi-site engineering teams, including direct management, mentorship, and technical talent development.
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Strong background in the end-to-end software development lifecycle, including requirements gathering, technical product ownership, business analysis, and agile delivery.
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Experience implementing strategic digital transformation and driving innovation using scaled Agile and DevOps methodologies.
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Expertise with data pipeline development, ETL processes, job scheduling, monitoring, and incident management across cloud and on-premises platforms.
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Deep understanding of GxP, validation, data privacy, and compliance requirements for pharmaceutical and biotechnology manufacturing environments.
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Demonstrated proficiency in Python, SQL, and/or other relevant programming languages for data engineering and analytics automation.
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Strong knowledge of AI/ML concepts and their practical application to manufacturing analytics (predictive analytics, anomaly detection, root cause analysis, etc.).
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Outstanding communication and collaborator engagement skills across all levels, including executive leadership.
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Experience managing budgets, vendor relationships, and external partnerships.
Preferred Qualifications:
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Doctorate in Computer Science, Engineering, Life Sciences, or related technical discipline.
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Certification in Databricks and AWS Data Engineering/Architecture.
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Expertise in manufacturing data platforms such as OSIsoft PI/AVEVA, batch reporting systems, and integration with MES, SAP, and automation platforms.
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Experience with advanced analytics visualization tools (Power BI, Tableau, Spotfire) and their integration with manufacturing data sources.
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Deep understanding of ISA-95 and ISA-88 manufacturing standards.
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Hands-on experience with cloud-based analytics, data lakes, data fabrics, and containerization (Kubernetes).
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Applied knowledge of DevOps practices, automated testing, and continuous integration/deployment.
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Domain expertise in health or life sciences, with a focus on manufacturing operations.
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Proficiency with graph databases, semantic modeling, and data science toolkits (R, Python, Oracle).
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Experience driving a collaborative, customer-focused culture that values technical depth, accountability, and service excellence.
Soft Skills -
Visionary leadership with a passion for nurturing technical talent and building high-performing analytics teams.
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Strategic thinking and adaptability in leveraging data and analytics in manufacturing.
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Exceptional organizational, problem-solving, and analytical skills.
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Resilience and composure in dynamic, fast-paced global environments.
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Commitment to diversity, inclusion, and fostering a collaborative, data-centric culture.
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Excellent verbal and written communication; strong presentation and public speaking abilities.
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High degree of initiative, motivation, and team orientation focused on achieving team goals.
