{"id":478518,"url":"https://alion.io/job/gibson-dunn-senior-data-engineer","title":"Senior Data Engineer","company":{"id":185365,"name":"Gibson Dunn","domain":"gibsondunn.com","url":"https://alion.io/company/gibson-dunn","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":95,"open_postings":26,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":69,"computed_at":"2026-10-29T05:49:05Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New York, United States","Washington, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":150000,"max":200000,"currency":"USD","period":"year","gross":null,"usd_annual":200000},"salary_estimate":null,"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Feature Store","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GCP","optional":false},{"name":"Machine Learning","optional":false},{"name":"Oracle ERP","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Power BI","optional":false},{"name":"PowerShell","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false}],"status":"live","first_seen_at":"2026-08-14T14:35:20Z","employer_posted_date":"2026-10-09","last_verified_at":"2026-10-29T08:11:22Z","board_verified":true,"closed_at":null,"days_open":75,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":75},"description":"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.\nBased 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.\nThis role reports to the Director, Product & Engineering.\nPrimary applications and platforms include:\nDocument Management: iManage (cloud), SPM, Litera CAM\nFinance: Aderant Expert Sierra, Chrome River, Time Entry\nHR: PeopleSoft, Workday\nEnterprise data lake, data warehouse, and analytics platforms\nResponsibilities include:\nData Platform, Data Lake & Pipeline Engineering\nDesign, build, and maintain scalable data lakes, warehouses, and lakehouse environments (on-premises and/or cloud) to consolidate data from diverse enterprise sources.\nDevelop and orchestrate reliable, automated ETL/ELT pipelines to ingest, transform, and deliver structured and unstructured data.\nImplement layered data architectures (e.g., raw / curated / consumption or bronze / silver / gold layers) that support reuse across reporting, analytics, and AI workloads.\nMonitor and maintain pipelines proactively to ensure high availability, timeliness, and data freshness.\nApply data quality, validation, and error-handling practices to ensure accuracy, completeness, and consistency.\nEstablish and maintain data lineage, cataloging, and metadata to support governance and traceability.\nData for AI / Machine Learning\nCollaborate with data scientists and ML practitioners to curate, prepare, and serve high-quality datasets for model training, fine-tuning, and inference.\nBuild and maintain pipelines that transform raw enterprise data into clean, model-ready datasets.\nSupport feature engineering, feature stores, and reusable data products for AI/ML use cases.\nEnable AI-oriented data patterns such as embedding pipelines and retrieval-augmented workflows, and support integration with vector stores where appropriate.\nPartner with engineering teams to operationalize data workflows that keep models supplied with reliable, well-governed data.\nDatabase Administration & Operational Support\nAdminister, monitor, and maintain relational database environments (on-premises and/or cloud).\nPerform and automate routine operations, including:Backups and restores (full, differential, and log).\nIntegrity checks and consistency validation.\nIndex maintenance and statistics updates.\n\nMonitor and troubleshoot performance issues, including CPU, memory, and I/O bottlenecks, as well as blocking, deadlocks, and long-running queries.\nImplement performance tuning strategies such as query optimization, execution plan analysis, and index design and review.\nManage database availability and resilience, including high availability, clustering, and disaster recovery planning and validation.\nCoordinate patching, upgrades, and service releases.\nEnsure security and compliance through access controls, permissions, encryption, auditing, and vulnerability mitigation.\nSupport scheduled jobs, ETL processes, and automated data workflows.\nQuery & Data Performance Optimization\nWrite efficient queries and transformations for reporting and analytical workloads.\nReduce dataset size and improve refresh and processing performance.\nUnderstand and optimize the impact of joins, filters, and aggregations across large datasets.\nAnalytics & Reporting\nTranslate business questions into queries, metrics, and visualizations.\nDevelop, maintain, and optimize dashboards and reports using leading BI tools (e.g., Tableau, Power BI, or comparable platforms).\nDesign semantic models and data sources for reporting, including fact/dimension modeling (star and snowflake schemas), data shaping, and transformation.\nOptimize report performance through query tuning and data model optimization (aggregations and relationships).\nCollaboration & Leadership\nParticipate actively in group and cross-functional meetings.\nDeliver clear, coherent report-outs to senior management.\nWork with interdepartmental groups to innovate and improve the firm’s data capabilities.\nMentor and train data engineers on the data platform and its business applications.\nQualifications:\nStrong analytical and problem-solving skills, with a track record of owning systems end-to-end.\nAbility to work independently and manage competing priorities in operational environments.\nEffective communication with both technical and business teams.\nProven ability to collaborate across departments to identify and drive improvements.\nExperience:\n10+ years of experience building and supporting enterprise data platforms, pipelines, and application databases.\nBachelor’s degree in Computer Science, Information Technology, Engineering, or a related field (or equivalent experience).\nExperience managing business-critical data systems in a global environment.\nProficiency with SQL and at least one programming/scripting language commonly used in data engineering (e.g., Python).\nHands-on experience with data warehousing, data lakes, or lakehouse architectures.\nExperience building ETL/ELT pipelines and working with data orchestration tools.\nExperience with cloud data platforms (e.g., AWS, Azure, Google Cloud, Snowflake, Databricks, or comparable).\nExperience preparing and serving data for AI/ML model training, fine-tuning, or inference.\nFamiliarity with distributed data processing frameworks (e.g., Apache Spark).\nFamiliarity with pipeline orchestration tools (e.g., Apache Airflow or similar).\nFamiliarity with ML and AI concepts, including feature stores, vector databases, and embedding/RAG pipelines.\nFamiliarity with automation and scripting (e.g., Python, PowerShell, or Bash).\nExposure to DevOps, MLOps, or CI/CD practices for data or BI deployments.\nGibson Dunn will consider for employment qualified Applicants with Criminal Histories in a manner consistent with the requirements of local law.\nCompensation & Benefits:\nThe annual compensation range for this position is $150,000 - $200,000. The salary offered within this range will depend upon qualifications and other operational considerations.\nBenefits offered for this position include health care; retirement benefits; paid days off, including sick time, and vacation time; parental leave; basic life insurance; Flexible Spending Accounts; as well as discretionary, performance-based bonuses.\n______\nFor technical difficulties with our online application, please contact us at . Our recruiting support team will respond as soon as possible.\n______\nGibson 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.\nIf you require a reasonable accommodation to complete an application, participate in an interview, or otherwise take part in the recruitment process, please contact us at . Please note, this is a dedicated email inbox established exclusively to assist applicants with accommodation request related to the recruitment process. 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