Data Engineer

Career Category Information Systems Job Description As a Data Engineer supporting Law data strategy , you will design, build, and maintain scalable data pipelines that integrate data from legal systems into Amgen’s enterprise data fabric . You will enable high-quality, governed datasets that support analytics, reporting, and emerging AI/ML use cases for Legal and Compliance teams.

This role requires strong hands-on engineering skills, familiarity with modern data platforms (e.g., Databricks), and the ability to work closely with Legal stakeholders, Data Architects, and AI/Analytics teams. Key Responsibilities Data Engineering & Pipeline Development Design, develop, and maintain data pipelines to ingest data from legal systems, third-party tools, and enterprise platforms Build and optimize ETL/ELT pipelines using modern frameworks (Databricks, Spark) Implement reliable, scalable, and production-ready data pipelines using engineering best practices, monitoring, and automated validation frameworks Integrate structured and unstructured legal data into the enterprise data fabric Ensure reliability, scalability, and performance of data pipelines Databricks & Modern Data Platform Develop pipelines using Databricks (Delta Lake, Spark, notebooks) Implement data transformation and orchestration workflows Support migration and modernization of legacy data solutions to cloud-native platforms Contribute to reusable data engineering patterns and components Optimize Delta Lake and Spark workloads for scalable, cost-efficient, and high-performance enterprise data processing Data Quality, Governance & Compliance Implement data quality checks, validation rules, and monitoring Implement governance, lineage, and security controls for sensitive legal and compliance datasets Ensure compliance with data governance, privacy, and legal/regulatory requirements (e.g., sensitive legal data handling) Maintain metadata, lineage, and documentation for legal datasets AI & Advanced Analytics Enablement Build curated datasets that support AI/ML models and GenAI use cases Prepare structured and unstructured datasets for AI/ML and GenAI use cases including document intelligence and semantic search applications Enable feature engineering and data preparation for AI applications in Legal (e.g., document analysis, contract insights) Collaborate with data scientists and AI teams to ensure data readiness and accessibility Collaboration & Delivery Work with Legal stakeholders to understand data needs and translate into technical solutions Partner with Data Architects to align with enterprise data fabric strategy Participate in Agile development processes (sprint planning, estimation, delivery) Document pipelines, models, and technical decisions Basic Qualifications Master's or Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field 5–8 years of experience in data engineering or related technical role Must-Have Technical Skills Strong experience with SQL and relational databases Programming experience in Python (required), PySpark preferred Hands-on experience with Databricks / Apache Spark Experience building ETL/ELT pipelines for large-scale datasets Familiarity with cloud platforms (AWS, Azure, or GCP) Understanding of data modeling and data warehousing concepts Preferred / Strategic Skills (Aligned to Future Data Strategy) Certification: Relevant certifications in Databricks, cloud platforms (AWS/Azure/GCP), or modern data engineering technologies are a plus Experience with: Delta Lake / Lakehouse architectures Data Fabric / Data Mesh concepts Snowflake, Redshift, or enterprise data warehouse platforms Familiarity with: Streaming data (Kafka, event-driven pipelines) Data orchestration tools (Airflow, Databricks Workflows) Exposure to: AI/ML data pipelines and feature engineering Unstructured data processing (documents, legal text) Understanding of: Data governance frameworks and cataloging tools Security and privacy controls for sensitive data (legal/compliance) Functional Skills Strong problem-solving and analytical thinking Ability to work with large, complex datasets Effective communication with both technical and non-technical stakeholders Ability to operate in a fast-paced Agile environment .