Career CategoryInformation SystemsJob DescriptionAs 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 ResponsibilitiesData Engineering & Pipeline DevelopmentDesign, develop, and maintain data pipelines to ingest data from legal systems, third-party tools, and enterprise platformsBuild 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 frameworksIntegrate structured and unstructured legal data into the enterprise data fabricEnsure reliability, scalability, and performance of data pipelinesDatabricks & Modern Data PlatformDevelop pipelines using Databricks (Delta Lake, Spark, notebooks)Implement data transformation and orchestration workflowsSupport migration and modernization of legacy data solutions to cloud-native platformsContribute to reusable data engineering patterns and componentsOptimize Delta Lake and Spark workloads for scalable, cost-efficient, and high-performance enterprise data processingData Quality, Governance & ComplianceImplement data quality checks, validation rules, and monitoringImplement governance, lineage, and security controls for sensitive legal and compliance datasetsEnsure compliance with data governance, privacy, and legal/regulatory requirements (e.g., sensitive legal data handling)Maintain metadata, lineage, and documentation for legal datasetsAI & Advanced Analytics EnablementBuild curated datasets that support AI/ML models and GenAI use casesPrepare structured and unstructured datasets for AI/ML and GenAI use cases including document intelligence and semantic search applicationsEnable 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 accessibilityCollaboration & DeliveryWork with Legal stakeholders to understand data needs and translate into technical solutionsPartner with Data Architects to align with enterprise data fabric strategyParticipate in Agile development processes (sprint planning, estimation, delivery)Document pipelines, models, and technical decisionsBasic QualificationsMaster's or Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field5-8 years of experience in data engineering or related technical roleMust-Have Technical SkillsStrong experience with SQL and relational databasesProgramming experience in Python (required), PySpark preferredHands-on experience with Databricks / Apache SparkExperience building ETL/ELT pipelines for large-scale datasetsFamiliarity with cloud platforms (AWS, Azure, or GCP)Understanding of data modeling and data warehousing conceptsPreferred / Strategic Skills (Aligned to Future Data Strategy)Certification:Relevant certifications in Databricks, cloud platforms (AWS/Azure/GCP), or modern data engineering technologies are a plusExperience with: Delta Lake / Lakehouse architecturesData Fabric / Data Mesh conceptsSnowflake, Redshift, or enterprise data warehouse platformsFamiliarity with: Streaming data (Kafka, event-driven pipelines)Data orchestration tools (Airflow, Databricks Workflows)Exposure to: AI/ML data pipelines and feature engineeringUnstructured data processing (documents, legal text)Understanding of: Data governance frameworks and cataloging toolsSecurity and privacy controls for sensitive data (legal/compliance)Functional SkillsStrong problem-solving and analytical thinkingAbility to work with large, complex datasetsEffective communication with both technical and non-technical stakeholdersAbility to operate in a fast-paced Agile environment.