Data Engineer MVA Inference And Audience Development Remote Global

📍 Note: Regardless of the location listed, this position is open to remote applicants worldwide. However, please be aware that our core working hours are centered around US Eastern Standard Time (EST), and candidates should have a schedule that allows for significant overlap with this time zone.

About RAVLCO

RAVLCO (Reliance Automated Verified Leads Company) is a modern, privacy-first technology platform that helps businesses grow through intelligent audience development and automated customer engagement. We combine powerful CRM tools, unified messaging, webchat, automated reviews, and data-driven lead tracking — all in one seamless platform — while maintaining the highest standards of compliance and ethical data use.

Our Mission

Our mission is simple: to build a better future where technology creates good jobs for everyone. We specialize in delivering precise, privacy-safe audience targeting and lead generation solutions that drive real business results without ever compromising trust or regulatory standards. Headquartered in the United States and fully remote-friendly, RAVLCO is where innovative marketing technology meets uncompromising data ethics.

About The Role

We are hiring a Data Engineer to build and maintain compliant, privacy-safe data infrastructure that powers audience development and marketing intelligence systems. This role ensures all data pipelines operate using aggregated, historical signals—never real-time incident or individual-level data.

Key Responsibilities

• Build and maintain ETL/ELT pipelines
• Develop reusable inference layers
• Create audit-ready segmentation workflows
• Implement governance controls (lineage, validation, logging)
• Perform geospatial aggregation (ZIP, county, corridor)
• Collaborate with Growth and Compliance teams
  
  

• Reliable, monitored pipelines
• Governed inference layer library
• Standardized segmentation workflows
• Improved audience performance
  
  

• Strong SQL and data modelling
• Python proficiency
• Cloud platforms (AWS, GCP, Azure)
• Data warehouses (Snowflake, BigQuery, Redshift)
• Data governance experience
• Aggregated geospatial data experience
  
  

• Understand inference vs. knowledge
• Use only aggregated historical data (45+ days old)
• No real-time or individual-level data usage
• Vendor diligence and audit logging
  
  

• Marketing segmentation experience
• Privacy frameworks (CCPA, GDPR, TCPA)
• dbt, Airflow, Dagster, Spark