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

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