Data Engineer
Company Overview
ActiveProspect is on a mission to make consent-based marketing the best channel for online customer acquisition. We provide marketers the products they need to acquire qualified customers at scale. Our platform is trusted by thousands of companies engaged in direct-to-consumer marketing, helping them save wasted spend, comply with ever-changing regulations, and manage a constantly evolving partner landscape. Our flagship product, TrustedForm, is used to certify over 1 billion opt-in digital customer leads every year and is the gold standard for documenting prior express written consent for TCPA compliance.
Job Summary
The Data Engineer is an individual-contributor role that designs, builds, and maintains scalable data pipelines and analytical data models. The role works hands-on with our modern data stack — SQL, Snowflake, dbt, Fivetran, Kafka, and Python — to build reliable, efficient data solutions. This is an entry-level role that grows toward full ownership of the data engineering workload over time.
Responsibilities:
Build, transform, and process datasets to keep data accurate, reliable, and available for analytics and business decisions. Maintain high-quality data infrastructure supporting reporting, analytics, and operational insights, including pipeline ingestion (Fivetran, Kafka), transformation and modeling (dbt, Snowflake), and Level 1 support for pipeline failures, monitoring, and data quality.
Qualifications and Skills
Reports to:
Organizational Impact
This role adds capacity to the data engineering function and reduces key-person risk by developing an engineer who can independently own the data engineering workload — ingestion, streaming, transformation, and data quality. As that ownership grows, it frees senior capacity to focus on higher-leverage architecture and modeling work.
Reliable, well-governed data is the foundation for ActiveProspect's reporting and decision-making. By keeping pipelines healthy and data trustworthy in Snowflake, this role directly supports the analytics and business teams that depend on accurate, timely data. Success looks like dependable data delivery, strong data quality, and faster resolution of issues through proactive monitoring and Level 1 support.