Senior Data Engineer

Gibson Dunn is a leading global law firm, advising clients on significant transactions and disputes. Our exceptional teams craft and deploy creative legal strategies that are meticulously tailored to every matter, however complex or high-stakes. The firm’s work is distinguished by a unique combination of precision and vision. Based in Washington D.C. or New York, the Senior Data Engineer will be responsible for designing, building, and operating the data platforms that power enterprise reporting, analytics, and artificial intelligence across the firm. The role spans the full data lifecycle: ingesting data from diverse operational systems, curating it within scalable data lakes and warehouses, and delivering high-quality, model-ready datasets to analysts, data scientists, and AI/ML workflows. This role blends hands-on data platform engineering with database and reporting expertise. This role reports to the Director, Product & Engineering . Primary applications and platforms include: Document Management: iManage (cloud), SPM, Litera CAM Finance: Aderant Expert Sierra, Chrome River, Time Entry HR: PeopleSoft, Workday Enterprise data lake, data warehouse, and analytics platforms Responsibilities include: Data Platform, Data Lake & Pipeline Engineering Design, build, and maintain scalable data lakes, warehouses, and lakehouse environments (on-premises and/or cloud) to consolidate data from diverse enterprise sources. Develop and orchestrate reliable, automated ETL/ELT pipelines to ingest, transform, and deliver structured and unstructured data. Implement layered data architectures (e.g., raw / curated / consumption or bronze / silver / gold layers) that support reuse across reporting, analytics, and AI workloads. Monitor and maintain pipelines proactively to ensure high availability, timeliness, and data freshness. Apply data quality, validation, and error-handling practices to ensure accuracy, completeness, and consistency. Establish and maintain data lineage, cataloging, and metadata to support governance and traceability. Data for AI / Machine Learning Collaborate with data scientists and ML practitioners to curate, prepare, and serve high-quality datasets for model training, fine-tuning, and inference. Build and maintain pipelines that transform raw enterprise data into clean, model-ready datasets. Support feature engineering, feature stores, and reusable data products for AI/ML use cases. Enable AI-oriented data patterns such as embedding pipelines and retrieval-augmented workflows, and support integration with vector stores where appropriate. Partner with engineering teams to operationalize data workflows that keep models supplied with reliable, well-governed data. Database Administration & Operational Support Administer, monitor, and maintain relational database environments (on-premises and/or cloud). Perform and automate routine operations, including: Backups and restores (full,…