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About the Role
The Senior Data Engineer will design, build, and operate data platforms for enterprise reporting, analytics, and AI. This role involves the full data lifecycle, from ingestion to delivering high-quality datasets for various applications, blending hands-on engineering with database and reporting expertise.
Responsibilities
- Design, build, and maintain scalable data lakes, warehouses, and lakehouse environments.
- Develop and orchestrate reliable, automated ETL/ELT pipelines.
- Implement layered data architectures to support reuse across reporting, analytics, and AI workloads.
- Monitor and maintain pipelines proactively for high availability, timeliness, and data freshness.
- Apply data quality, validation, and error-handling practices.
- Establish and maintain data lineage, cataloging, and metadata.
- Curate, prepare, and serve high-quality datasets for AI/ML 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 and support integration with vector stores.
- Partner with engineering teams to operationalize data workflows.
- Administer, monitor, and maintain relational database environments.
- Perform and automate routine database operations, including backups, integrity checks, and index maintenance.
- Monitor and troubleshoot database performance issues.
- Implement performance tuning strategies for databases.
- Manage database availability and resilience, including high availability, clustering, and disaster recovery.
- Coordinate patching, upgrades, and service releases for databases.
- Ensure security and compliance through access controls, permissions, encryption, auditing, and vulnerability mitigation.
- Support scheduled jobs, ETL processes, and automated data workflows.
- Write efficient queries and transformations for reporting and analytical workloads.
- Reduce dataset size and improve refresh and processing performance.
- Understand and optimize the impact of joins, filters, and aggregations across large datasets.
- Translate business questions into queries, metrics, and visualizations.
- Develop, maintain, and optimize dashboards and reports using leading BI tools.
- Design semantic models and data sources for reporting.
- Optimize report performance through query tuning and data model optimization.
- Participate actively in group and cross-functional meetings.
- Deliver clear, coherent report-outs to senior management.
- Work with interdepartmental groups to innovate and improve the firm’s data capabilities.
- Mentor and train data engineers on the data platform and its business applications.
Requirements
- Strong analytical and problem-solving skills, with a track record of owning systems end-to-end.
- Ability to work independently and manage competing priorities in operational environments.
- Effective communication with both technical and business teams.
- Proven ability to collaborate across departments to identify and drive improvements.
- 10+ years of experience building and supporting enterprise data platforms, pipelines, and application databases.
- Experience managing business-critical data systems in a global environment.
- Proficiency with SQL and at least one programming/scripting language commonly used in data engineering (e.g., Python).
- Hands-on experience with data warehousing, data lakes, or lakehouse architectures.
- Experience building ETL/ELT pipelines and working with data orchestration tools.
- Experience with cloud data platforms (e.g., AWS, Azure, Google Cloud, Snowflake, Databricks, or comparable).
- Experience preparing and serving data for AI/ML model training, fine-tuning, or inference.
- Familiarity with distributed data processing frameworks (e.g., Apache Spark).
- Familiarity with pipeline orchestration tools (e.g., Apache Airflow or similar).
- Familiarity with ML and AI concepts, including feature stores, vector databases, and embedding/RAG pipelines.
- Familiarity with automation and scripting (e.g., Python, PowerShell, or Bash).
- Exposure to DevOps, MLOps, or CI/CD practices for data or BI deployments.
Skills
- SQL
- Python
- Data Warehousing
- Data Lakes
- Lakehouse Architectures
- ETL/ELT Pipelines
- Data Orchestration Tools
- Cloud Data Platforms (AWS, Azure, Google Cloud, Snowflake, Databricks)
- AI/ML Data Preparation
- Apache Spark
- Apache Airflow
- Feature Stores
- Vector Databases
- Embedding/RAG Pipelines
- Automation
- Scripting (Python, PowerShell, Bash)
- DevOps
- MLOps
- CI/CD
- Tableau
- Power BI
Location
- US
Work Type
- Full-time
Experience Level
- Senior
- 10+ years
Education Level
- Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field (or equivalent experience)
Salary/Compensations
- $150,000 - $200,000
Benefits
- Health care
- Retirement benefits
- Paid days off, including sick time, and vacation time
- Parental leave
- Basic life insurance
- Flexible Spending Accounts
- Discretionary, performance-based bonuses
About the Company
- 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.
Equal Opportunity
- Gibson Dunn will consider for employment qualified Applicants with Criminal Histories in a manner consistent with the requirements of local law.
- Gibson Dunn is committed to ensuring equal employment opportunities for all qualified applicants, including individuals with disabilities.
- We strive to ensure an inclusive and accessible hiring experience.
- The Firm will provide reasonable accommodations to qualified individuals with disabilities to enable participation in the application and recruitment process, unless doing so would impose an undue hardship, in accordance with applicable laws and regulations.