Senior Data Engineer

TruLegal (formerly TRU Staffing); United States; Remoto; USD 145.000 - 165.000; Tempo pieno

Descrizione del lavoro

A senior data engineer role at a leading global law firm is seeking a hands-on expert to design, build, and operate enterprise data platforms supporting reporting, analytics, and AI/ML initiatives. The position emphasizes data lakes, warehouses, ETL/ELT pipelines, SQL optimization, and preparing high-quality model-ready datasets.

The role is primarily remote with a requirement to reside within commuting distance of a U.S.

Competenze

• Bachelor’s degree in Computer Science, IT, Engineering, or related field (or equivalent experience).
• Extensive experience building and supporting enterprise data platforms, pipelines, and databases.
• Strong SQL proficiency and scripting (Python) skills.
• Experience with cloud data platforms (AWS, Azure, GCP) and data lake/warehouse architectures.
• Knowledge of data governance, lineage, and metadata practices.
• Familiarity with ML data workflows and feature stores.

Mansioni

• Design, build, and maintain scalable data lakes, warehouses, and lakehouse environments.
• Develop and orchestrate ETL/ELT pipelines to ingest and transform data.
• Collaborate with data scientists to prepare model-ready datasets.
• Optimize query performance and data models for analytics and BI.
• Maintain data quality, lineage, and governance across platforms.
• Mentor and guide junior data engineers.

Conoscenze

SQL

Python

Data modeling

ETL/ELT design

Cloud data platforms

Data pipelines

Formazione

Bachelor’s degree or equivalent experience

Strumenti

Airflow

Spark

Tableau/Power BI

Descrizione del lavoro

Our client, a leading global law firm, is seeking a Senior Data Engineer to design, build, and operate enterprise data platforms supporting reporting, analytics, and AI/ML initiatives. This hands‑on role, high‑level IC roles will focus on data lakes and warehouses, ETL/ELT pipelines, SQL and data performance optimization, database operations, and preparing high‑quality, model‑ready datasets for AI/ML workflows. Ideal candidates will bring extensive data engineering experience, strong SQL and scripting/programming capabilities, cloud data platform expertise, and experience supporting data for AI/ML use cases. The role is primarily remote, but candidates must live within commuting distance of one of the firm's U.S. offices.

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, 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.

Query & Data Performance Optimization

• 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.

Analytics & Reporting

• 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).

Collaboration & Leadership

• 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.

Qualifications

• 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.

Experience

• 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.

Expected salary for this role is $145,000 - $165,000, commensurate with experience, training, skills, qualifications, and other market factors.