Greenberg Traurig (GT), a global law firm with locations across the world in 15 countries, has an exciting employment opportunity for you. We offer competitive compensation and an excellent benefits package, along with the opportunity to work within an innovative and collaborative environment.
Join our Innovation Team as a Legal Data Analyst Lead in one of our various U.S. office locations. We are seeking a highly skilled professional who thrives in a fast-paced, deadline-driven environment. The ideal candidate possesses strong problem-solving and decision-making abilities, ensuring efficiency and accuracy in every task. With a dedicated work ethic and a can-do attitude, you will take initiative and approach challenges with confidence and resilience. Excellent communication skills are essential for collaborating effectively across teams and delivering exceptional client service. If you are someone who demonstrates initiatives, adaptability, and innovation, we invite you to join our team.
Position Summary:The Legal Data Engineer Lead is responsible for designing, developing, and maintaining data-driven and AI-enabled solutions that support legal matters, client engagements, and internal business initiatives. This role oversees complex data engineering, integration, automation, analytics, and AI projects involving large, diverse, and often unstructured data sets, leveraging advanced technical expertise to deliver scalable, defensible, and high-quality solutions. The position exercises independent judgment in selecting appropriate technologies and methodologies, evaluates the accuracy and reliability of AI-assisted outputs, identifies opportunities to enhance workflows through automation and AI, and provides technical guidance to team members.
Key Responsibilities:- Designs, develops, tests, and maintains complex data workflows and pipelines incorporating traditional data engineering, automation, and AI-enabled processing techniques.
- Develops Python-based solutions for data processing, extraction, classification, transformation, validation, reconciliation, and reporting.
- Designs workflows that use generative AI, large language models, multimodal AI, or other AI technologies to extract, classify, summarize, normalize, or structure information from PDFs, documents, images, spreadsheets, and other structured and unstructured data sources.
- Develops structured AI workflows using techniques such as prompt engineering, schema-based outputs, JSON processing, validation logic, exception handling, and automated quality-control procedures.
- Integrates AI capabilities with Python, APIs, databases, cloud services, and traditional data-processing workflows to create repeatable and scalable analytical solutions.
- Evaluates AI-generated outputs for accuracy, completeness, consistency, and reliability and develop validation procedures appropriate for legal and client-facing work.
- Designs human-in-the-loop review processes and other quality-control mechanisms for AI-assisted workflows where appropriate.
- Identifies appropriate and inappropriate use cases for AI based on data sensitivity, analytical risk, accuracy requirements, confidentiality, and the intended use of the resulting work product.
- Assists in developing standards, documentation, and governance practices for the responsible use of AI within Legal Data Analytics workflows.
- Evaluates emerging AI technologies, models, platforms, and development approaches and recommend tools that can improve the efficiency, accuracy, or scalability of the department's analytical services.
- Prototypes and developsAI-enabled tools and self-service applications that automate repetitive data preparation, extraction, review, or analytical processes.
- Collaborates with attorneys and technical professionals to translate legal and business requirements into data engineering and AI-assisted analytical solutions.
- Provides technical guidance to team members on data extraction, transformation, automation, reporting, data validation, and analytics tools and processes.
Qualifications:Skills & Competencies
- Strong attention to detail, organizational skills, and commitment to data accuracy and quality assurance.
- Strong collaboration and client service orientation with the ability to partner effectively across departments and business functions.
- Strong analytical, problem-solving, and critical thinking skills with the ability to work with complex datasets and identify data quality issues, trends, and anomalies.
- Strong Python programming skills with demonstrated experience developing reusable data-processing, automation, and AI-enabled workflows.
- Practical experience incorporating generative AI or large language models into data-processing or analytical workflows.
- Understanding of prompt engineering and techniques for producing reliable, structured outputs from generative AI systems.
- Experience working with structured AI outputs, including JSON schemas, parsing, validation, exception handling, and downstream data transformation.
- Familiarity with multimodal AI techniques for processing documents, PDFs, images, or other unstructured information.
- Ability to evaluate AI-generated results critically and design validation and quality-control procedures to identify hallucinations, omissions, inconsistencies, and other output errors.
- Understanding of human-in-the-loop design and the importance of maintaining appropriate human review for higher-risk legal and analytical applications.
- Ability to determine when traditional programming, deterministic rules, statistical methods, or AI-based approaches are most appropriate for a particular analytical problem.
- Familiarity with responsible AI concepts, including data privacy, confidentiality, security, transparency, reproducibility, and appropriate use of AI within professional services environments.
- Ability to prototype new AI-enabled analytical workflows and move successful concepts toward repeatable production processes.
Education & Prior Experience
- Bachelor's degree in Data Analytics, Data Science, Computer Science, Information Systems, Statistics, Mathematics, Engineering, or a related quantitative or technical field required; equivalent combination of certifications and relevant professional experience may be considered
- Minimum of 5 years of progressively responsible experience in data engineering, data analytics, database development, automation, business intelligence, or a related technical discipline.
- Demonstrated Data extraction and document-processing techniques
- Demonstrated Data validation, reconciliation, and automated quality control
- Demonstrated experience using Python and SQL to develop complex data-processing and automation solutions.
- Demonstrated experience incorporating artificial intelligence, generative AI, machine learning, natural language processing, or related technologies into data or document-processing workflows.
- Experience independently developing solutions involving structured and unstructured data, including spreadsheets, databases, PDFs, documents, images, APIs, or other source formats.
- Experience evaluating and validating automated or AI-generated outputs in environments where accuracy and reproducibility are important.
- Experience supporting data-intensive environments, preferably within a law firm, legal services, professional services, consulting, compliance, human resources, financial, litigation, or employment analytics setting.
- Experience mentoring, reviewing, or providing technical guidance to less experienced technical professionals preferred.
- Development of AI-enabled self-service applications or analytical tools
Technology
Required:
- Advanced Microsoft SQL Server and T-SQL
- Python
- Microsoft Excel
- Generative AI / Large Language Models
- Prompt engineering
- REST APIs and JSON
- Structured-output and schema-based AI workflows
- Git or comparable source-control practices
Preferred:
- Microsoft Azure
- Microsoft Fabric
- Azure Data Factory
- Azure SQL Database
- Azure AI services
- Azure OpenAI or comparable enterprise LLM platforms
- Power BI
- SQL Server Integration Services (SSIS)
- Multimodal AI and document intelligence technologies
- Retrieval-augmented generation (RAG) concepts
- Vector search or semantic search concepts
- AI orchestration or agent-based workflow concepts
- Machine learning or natural language processing
Physical Requirements:- While performing the duties of this job, the employee is occasionally required to move from workstation or desk throughout the work area to work independently or with a team to meet with colleagues or supervisor and retrieve work assignments
- This position may also be sedentary and require the employee to sit for extended periods of time
- Requires manual dexterity to dial a telephone, enter data into a computer, handle objects, and operate tools
GT is an EEO employer with an inclusive workplace committed to merit-based consideration and review without regard to an individual's race, sex, or other protected characteristics and to the principles of non-discrimination on any protected basis.
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