Job Title: Data Engineer – MVA Inference & Audience Development (Remote) Company: Liberty Legal Network Location: Remote (Global) Job Type: Full-Time About Liberty Legal Network Liberty Legal Network is a legal technology platform that uses AI-powered matching to connect individuals with specialized law firms nationwide. Our mission is to simplify access to legal representation while improving outcomes through intelligent data, precision matching, and scalable technology. About the Role We’re looking for a Data Engineer to design and build scalable, privacy-first data systems that power audience development and marketing intelligence. In this role, you’ll develop reliable data pipelines and inference systems using aggregated, historical data—ensuring all infrastructure meets strict compliance and governance standards. You’ll collaborate closely with Growth and Compliance teams to deliver high-quality, actionable insights. What You’ll Do - Design, build, and maintain ETL/ELT data pipelines - Develop reusable inference layers for audience modeling - Create standardized, audit-ready segmentation workflows - Implement data governance practices (lineage, validation, logging) - Perform geospatial aggregation (ZIP, county, corridor-level data) - Partner with Growth and Compliance teams to support data-driven initiatives What Success Looks Like (First 60–90 Days) - Production-ready, monitored data pipelines - Established and governed inference layer library - Consistent, scalable segmentation workflows - Measurable improvements in audience targeting performance What You Bring - Strong SQL skills and experience with data modeling - Proficiency in Python - Experience with cloud platforms (AWS, GCP, or Azure) - Familiarity with modern data warehouses (Snowflake, BigQuery, Redshift) - Experience implementing data governance and quality controls - Experience working with aggregated or geospatial datasets Compliance & Data Standards - Work exclusively with aggregated, historical datasets (45+ days old) - No use of real-time or individual-level data - Apply best practices in data privacy, governance, and auditability - Understand the distinction between inferred insights and direct knowledge - Support vendor compliance and data audit processes Nice to Have - Experience with marketing or audience segmentation systems - Familiarity with privacy frameworks (CCPA, GDPR, TCPA) - Experience with tools such as dbt, Airflow, Dagster, or Spark Compensation Compensation is competitive and based on experience, skills, and location. Why Join Us - Work on impactful technology that improves access to legal services - Build privacy-first data systems in a rapidly evolving space - Collaborate with cross-functional teams in a fully remote environment - Opportunity to shape scalable data infrastructure from the ground up We are committed to equal opportunity and diversity in the workplace. We consider all qualified applicants without regard to race, color, religion, gender, sexual orientation, national origin, disability, or veteran status. All inquiries and applications are kept confidential.

• Build and maintain data pipelines and infrastructure that power data-driven decisions across the firm. • Work within the Data Science team, collaborating with analysts, scientists, and stakeholders to ensure high-quality, accessible data. • Design, build, and optimize ETL pipelines using Microsoft Fabric and Medallion Architecture. • Identify and integrate new data sources; assemble complex datasets aligned to business needs. • Improve infrastructure scalability, data delivery, and process automation. • Monitor data quality, troubleshoot issues, and enforce governance and security standards. • Support business intelligence tools and data models; collaborate with Executive Leadership, App Dev, Marketing, and Finance. • Maintain documentation; participate in monthly on-call rotation.

• Execute data and document migrations for Evoke™ platform implementations, including data extraction, transformation, and loading (ETL) into Evoke solutions. • Review data mapping documentation and contribute to the design of conversion methodologies that enable accurate and efficient data transformation and loading. • Research and evaluate automated and AI-driven tools to enhance data migration processes, championing adoption within the organization. • Develop and deploy integration solutions for Evoke™ platform projects, including API-based and batch-based data imports and exports. • Provide input and guidance to customer data teams and internal consultants during data mapping activities, ensuring alignment with migration and integration requirements. • Collaborate with internal teams to gather data from multiple repositories and create models and visualizations that support operations management. • Design, build, and maintain scalable data pipelines and workflows to ensure efficient data processing. • Ensure data integrity, quality, and security throughout migration and integration processes. • Troubleshoot and resolve data-related issues during implementation and internal projects. • Document processes, standards, and best practices for data engineering and integration activities.

• Diseñar, desarrollar y mantener pipelines de ingestión y transformación de datos en entornos productivos. • Garantizar la estabilidad y fiabilidad de los flujos de datos, asegurando SLAs, mecanismos de retry, monitorización y alertas. • Definir y aplicar patrones de ingestión (batch, incremental, backfills) alineados con las necesidades del negocio. • Optimizar el rendimiento y los costes de los procesos de datos en BigQuery y en los servicios asociados en GCP. • Colaborar estrechamente con Analytics Engineers para asegurar una integración correcta entre la capa de ingestión y la capa analítica. • Participar en el diseño y la evolución de la arquitectura de datos del grupo. • Implementar controles de calidad de datos y validaciones técnicas en los pipelines. • Definir y promover buenas prácticas de desarrollo, versionado, testing y documentación en Git. • Contribuir activamente a la mejora continua de la plataforma de datos junto al Data Team Lead.