As aData EngineersupportingLaw data strategy, you will design, build, and maintain scalable data pipelines that integrate data from legal systems into Amgensenterprise data fabric.

You will enable high-quality, governed datasets that supportanalytics, reporting, and emerging AI/ML use casesfor 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 fromlegal systems, third-party tools, and enterprise platforms
- Build and optimizeETL/ELT pipelinesusing 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 theenterprise data fabric
- Ensure reliability, scalability, and performance of data pipelines

Databricks Modern Data Platform

- Develop pipelines usingDatabricks (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 withdata 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 supportAI/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 Bachelors degree in Computer Science, Engineering,



Information Systems, or related field
- 58 yearsof experience in data engineering or related technical role

Must-Have Technical Skills

- Strong experience withSQLand relational databases
- Programming experience inPython (required), PySpark preferred
- Hands-on experience withDatabricks / Apache Spark
- Experience buildingETL/ELT pipelinesfor large-scale datasets
- Familiarity withcloud platforms (AWS, Azure, or GCP)
- Understanding ofdata modeling and data warehousing concepts

Preferred / Strategic Skills (Aligned to Future Data Strategy)

- Certification:
- Relevant certifications in Databricks, cloud platforms (AWS/Azure/GCP), or up-to-date 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

📌 Data Engineer (Hyderabad)
🏢 Amgen
📍 Hyderabad