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 Amgens 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 Bachelors degree in Computer Science, Engineering, Information Systems, or related field.

- 5 to 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 :

1. Relevant certifications in Databricks, cloud platforms (AWS/Azure/GCP), or modern data engineering technologies are a plus.

- Experience with :

1. Delta Lake / Lakehouse architectures.

2. Data Fabric / Data Mesh concepts.

3. Snowflake, Redshift, or enterprise data warehouse platforms.

- Familiarity with :

1. Streaming data (Kafka, event-driven pipelines).

2. Data orchestration tools (Airflow, Databricks Workflows).

- Exposure to :

1. AI/ML data pipelines and feature engineering.

2. Unstructured data processing (documents, legal text).

- Understanding of :

1. Data governance frameworks and cataloging tools.

2. 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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