Data Engineer – MVA Inference & Audience Development (Remote, Global)

Reliance Automated Verified Leads, LLC; Indonesia; On-site; IDR 180,000,000 - 320,000,000; Full time

Job summary

Reliance Automated Verified Leads, LLC is seeking a Data Engineer to build and maintain compliant, privacy-safe data infrastructure powering audience development and marketing intelligence systems. The role emphasizes aggregated, historical signals rather than real-time or individual data.

Key responsibilities include ETL/ELT pipeline work, reusable inference layers, governance, and geospatial aggregation, with collaboration across Growth and Compliance teams to ensure governance and security

Qualifications

• Strong SQL and data modelling skills.
• Proficiency in Python.
• Experience with cloud platforms (AWS, GCP, Azure).
• Experience with data warehouses Snowflake, BigQuery, Redshift.
• Data governance experience and auditable pipelines.
• Experience with aggregated geospatial data.

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.

Skills

SQL

Data Modelling

Python

Cloud Platforms

Snowflake

BigQuery

Redshift

Data Governance

Geospatial Data

Tools

dbt

Airflow

Dagster

Spark

Job description

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.

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 libraryStandardized 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.