Legend Biotech is seeking a Lead Data Engineer as part of the Information Technology team based in Bridgewater, NJ.

Role Overview

We are seeking a Lead Data Engineer to build and operate enterprise-grade data pipelines using Microsoft Azure as the orchestration layer and Snowflake as the enterprise data lake/warehouse. This role focuses on reliable ETL/ELT, push/pull data integration patterns, and scalable platform engineering in a GxP / validated environment.

Key Responsibilities

• The Lead Data Engineer will be responsible for developing and maintaining ETL/ELT pipelines for ingesting data from various sources into our data warehouse.
• Design and build batch and incremental data pipelines using Azure-native orchestration services to move data into and out of Snowflake.
• Support push and pull architectures across files, APIs, enterprise applications, and external partners.
• Develop curated, consumption-ready datasets in Snowflake using standardized, reusable patterns.
• Implement data quality, and reconciliation controls within pipelines.
• Follow CI/CD and controlled deployment practices across Dev / QA / Prod environments.
• Ensure solutions meet GxP expectations for data integrity, traceability, auditability, and change control.
• Partner with Quality, Validation, and platform teams to support compliance and audit readiness.
• Data Catalog, Metadata & Governance Enablement:
  • Publish and maintain data assets, schemas, and lineage in the enterprise data catalog.
  • Partner with Data Governance and Business teams to support certified datasets, ownership, and usage transparency.
• Work with an end-to-end ownership mindset, innovate and drive initiatives through completion.
• Optimize data storage and retrieval to ensure efficient performance and scalability.
• Collaborate with data architects, data analysts and data scientists to understand their data needs and ensure that the data infrastructure supports their requirements.
• Ensure data quality and integrity through data validation and testing.
• Implement and maintain security protocols to protect sensitive data.
• Stay up-to-date with emerging trends and technologies in data engineering and analytic.
• Community lead to enable adoption of data and technology strategy.
• Knowledgeable in evolving trends in Data platforms and Product based implementation.
• Comfortable working in a fast-paced environment with minimal oversight.
• Prior experience working in an Agile/Product based environment.
• Data engineering, pipelines, AI/ML development, requirements gathering with Business Partners.
• Technical delivery execution (build, test, deploy).
• Technical estimates and feasibility.
• Resource planning within Data & AI team.
• Qualify/Validate system and pipelines.
• FUT/SIT completion.
• Technical Project Management.

Requirements

• Bachelor's degree in Computer Science, Engineering, Information Systems, or related field (Master's preferred).
• 7-10 years of hands-on data engineering experience.
• Total 15+ years of IT experience
• Strong experience with Microsoft Azure orchestration/integration services.
• Strong experience working with Snowflake as a data lake/warehouse.
• Proven experience with ETL/ELT and push/pull integration patterns.
• Experience operating in regulated or validated (GxP) environments.
• Strong SQL and data engineering fundamentals.
• Experience with enterprise data platforms and multi-domain data architectures.
• Familiarity with CI/CD, role-based access control, and secure data sharing.
• Experience in Data Catalog tools and implementation.
• Experience integrating ERP, manufacturing, or regulated systems.
• Strong programming skills (Python, SQL; Java/Scala a plus)
• Experience with data warehousing and lakehouse architectures.
• Hands-on experience with ETL/ELT tools and orchestration frameworks.
• Proficiency with relational and non-relational databases.
• Strong understanding of data modeling, normalization, and performance tuning.
• Cloud data platforms (e.g., Azure Synapse, Snowflake, BigQuery, Redshift).
• Data processing frameworks (e.g., Spark).
• Orchestration tools (e.g., Airflow).
• Version control and CI/CD practices.

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