Data engineer – MVA inference & audience development

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
  

Success (60–90 Days)

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

Required Qualifications

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

Compliance Expectations

• 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
  

Nice to Have

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

Compensation

• Compensation is based on experience, skills, and location and will be competitive within the global market.

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
  

Success (60–90 Days)

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

Required Qualifications

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

Compliance Expectations

• 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
  

Nice to Have

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

Compensation

• Compensation is based on experience, skills, and location and will be competitive within the global market.