Data Engineer
About this opportunity
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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Job details
How this role compares
Computed from every other active Data & Digital role in our database, not just this employer's listings.
We currently track 292 comparable Data & Digital roles across 41 biopharma companies.
Salary context
60 of 292 peers report a salary range (USD, annualized)
Peers share this role's job function. This posting doesn't list a seniority level, so peers aren't narrowed by seniority either -- the range below may span more levels than usual.
Where these roles are based
Top locations among the 292 comparable roles
+ 14 more countries
Seniority mix
158 of 292 peers have a known seniority level
Therapeutic area mix
6 of 292 peers have a known therapeutic area; the rest are genuinely unlabeled, not hidden
Similar opportunities
The closest matches from our peer group, ranked by how similar they are, not how well you'd qualify for them -- treat this as market context, not a guaranteed shortlist; a weak match is labeled as one below.
How we calculate "similar"
No black box, no LLM guesswork: a deterministic score built from four normalized attributes. Here's this role's own peer group at different match levels, so you can see the mechanism, not just the result.
Every comparison starts from the same 100-point budget: 25 for working in the same function, 40 for the same therapeutic area, 20 for the same or adjacent seniority, 15 for the same country. A dimension we can't confirm on both sides contributes nothing, never a guess, never a free pass.
0 points, never a partial guess. A role we know almost nothing about beyond its function bottoms out at 25%; it never inflates to 100% just because there's little to compare against. Seniority uses a defined ladder (Associate → Manager → Associate Director → Senior → Principal → Director → Senior Director → Executive/VP) so "Director" and "Senior Director" count as adjacent, but "Director" and "Executive/VP" do not.