Note: The job is a remote job and is open to candidates in USA. TruLegal is recruiting for a leading global law firm seeking a Senior Data Engineer. The role designs, builds, and operates enterprise data platforms, data lakes, warehouses, ETL/ELT pipelines, and database environments supporting reporting, analytics, and AI/ML initiatives. Responsibilities also include data performance optimization, dashboard and report development, operational support, cross-functional collaboration, and mentoring.
Responsibilities
- 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
- 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
- Administer, monitor, and maintain relational database environments (on-premises and/or cloud)
- Backups and restores (full, differential, and log)
- Integrity checks and consistency validation
- Index maintenance and statistics updates
- Monitor and troubleshoot performance issues, including CPU, memory, and I/O bottlenecks, as well as blocking, deadlocks, and long-running queries
- Implement performance tuning strategies such as query optimization, execution plan analysis, and index design and review
- Manage database availability and resilience, including high availability, clustering, and disaster recovery planning and validation
- Coordinate patching, upgrades, and service releases
- 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 (e.g., Tableau, Power BI, or comparable platforms)
- Design semantic models and data sources for reporting, including fact/dimension modeling (star and snowflake schemas), data shaping, and transformation
- Optimize report performance through query tuning and data model optimization (aggregations and relationships)
- 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
Skills
- The role is primarily remote, but candidates must live within commuting distance of one of the firm's U.S. offices
- 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
- Extensive experience building and supporting enterprise data platforms, pipelines, and application databases
- Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field (or equivalent experience)
- 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
Benefits
- The role is primarily remote
Company Overview