Sr. Associate Data Engineer

In this vital role play a key role in a regulatory submission content automation initiative which will modernize and digitize the regulatory submission process, positioning Amgen as a leader in regulatory innovation. The initiative demonstrates innovative technologies, including Generative AI, Structured Content Management, and integrated data to automate the creation, review, and approval of regulatory content.

The role is responsible for sourcing and analyzing data and supporting designing, building, and maintaining the data pipelines, and data models including semantic models that enable structured, reusable, and ontology-compliant data representations to drive business actions and automation. The ideal candidate has strong technical skills, experience with big data technologies, and a deep understanding of data architecture, ETL processes, and transform raw data into semantically enriched formats that support AI, analytics, and regulatory submissions.

Roles & Responsibilities:

• Design and build conceptual and logical data models, including 3NF schemas and Entity Relationship Diagrams (ERDs) using tools like Lucidchart.
• Transform structured and unstructured data into reusable semantic bricks aligned with enterprise vocabularies and taxonomies.
• Collaborate with cross-functional teams to support semantic ingestion, transformation, and integration of models; contribute to SPARQL and SQL script generation for ontology-to-relational mapping.
• Identify, extract, and integrate required business data from Operations systems residing in modern cloud-based architectures.
• Design, develop, test and maintain scalable data pipelines, ensuring data quality via ETL/ELT processes.
• Schedule and manage workflows to ensure pipelines run on schedule and are monitored for failures.
• Implement data integration solutions and manage end-to-end pipeline projects, including scope, timelines, and risk.
• Reverse-engineer schemas and explore source system tables to map local representations of target business concepts.
• Navigate application UIs and backends to gain business domain knowledge and detect data inconsistencies.
• Break down information models into fine-grained, business-contextualized data components.
• Work closely with multi-functional teams, including product teams, data architects, and business SMEs, to understand requirements and design solutions.
• Participate in KT sessions and maintain confluence documentation for semantic processes and architecture.
• Support end-to-end regulatory workflows by ensuring semantic consistency and traceability across data domains.
• Create and maintain data models, dictionaries, and documentation to ensure accuracy and consistency.
• Adhere to SOPs, GDEs, and standard methodologies for coding, testing, and reusable component design.

Masters degree and 3 to 7 years of Computer Science, IT or related field experience OR

Bachelors degree and 5 to 9 years of Computer Science, IT or related field experience OR

Diploma and 7 to 11 years of Computer Science, IT or related field experience

Preferred Qualifications:

Must-Have Skills:

• Hands-on experience with data practices, technologies, and platforms, such as Databricks, Python, Gitlab, LucidChart etc
• Proficiency in data analysis tools (eg. SQL) and experience with data sourcing tools
• Understanding of data governance frameworks, tools, and standard methodologies.
• Knowledge of and experience with data standards (FAIR) and protection regulations and compliance requirements (e.g., GDPR, CCPA)
• Data Modelling experience with proficiency in conceptual & logical data modelling

• Understanding of ontology design, and semantic modelling.
• Ability to work in agile environments and collaborate across matrixed teams.
• Knowledge of data lifecycle management, data governance, and metadata standards.
• Strong understanding of data modeling, data warehousing, and data integration concepts
• Knowledge of Python/R, Databricks, cloud data platforms

• Proficiency in SPARQL, SQL, Lucidchart, and TopBraid Composer.
• Experience with semantic layer platforms (e.g., MarkLogic, RDF stores).
• Understanding of GxP compliance and pharmaceutical data standards.
• Familiarity with regulatory data domains (e.g., CMC, Batch/Lot, Composition).
• Experience with ETL tools and various Python packages related to data processing, machine learning model development

• Preferable to have Semantic Web Developer Certification (W3C), Certified Knowledge Engineer (CKE)
• Certified Data Engineer / Data Analyst (preferred on Databricks)

• Excellent critical-thinking and problem-solving skills
• Strong communication and collaboration skills
• Demonstrated awareness of how to function in a team setting
• Demonstrated presentation skills