Data Engineer

Remote

Full Time

Experienced

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.

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Please describe your experience building data pipelines in a cloud environment (AWS, GCP, or Azure). Include the types of pipelines you’ve built, tools used, and scale/complexity.*

Which data warehouses have you worked with (e.g., Snowflake, BigQuery, Redshift, or others)? Please describe how you’ve used them in your projects.*

How would you describe your proficiency in SQL, particularly for data modeling and transformation? Please provide examples of complex queries or models you’ve built.*

Describe your experience using Python for data processing, automation, or pipeline development. Include specific libraries, frameworks, or use cases.*

Tell us about your experience implementing data governance practices such as data lineage, validation checks, or audit logging. What tools or approaches have you used?*

Describe any experience you have working with aggregated or geospatial data (e.g., ZIP code, county, or regional segmentation). How did you structure or use this data?*

This role requires working exclusively with aggregated, historical data (45+ days old) and avoiding real-time or individual-level data. How have you worked within similar constraints, or how would you approach this requirement?*

Describe any experience you have working with audience segmentation, marketing data systems, or marketplace/lead matching platforms. What was your role and impact?*

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