Build the enrichment platform - Design and scale pipelines that process 100M+ contact records, integrating with large-scale contact data vendors.
Own data quality - Build deduplication, entity resolution, and record matching systems that merge contacts from multiple sources into a single high-quality record.
Optimize vendor economics - Create waterfall enrichment logic that maximizes coverage and freshness while minimizing per-record cost across a portfolio of data vendors.
Ship freshness infrastructure - Build systems that detect job changes, flag stale records, and trigger re-enrichment to keep the dataset current.
Instrument and measure - Create quality scoring, coverage dashboards, and accuracy metrics that give the business visibility into dataset health.

Requirements

5+ years of data engineering experience, including 2+ years working with contact data, enrichment systems, or data infrastructure.
Deep experience with data vendor APIs and the economics of contact data (coverage rates, accuracy tradeoffs, cost per record).
Expert-level SQL and data modeling skills, with experience designing schemas for large-scale entity datasets.
Track record building production data pipelines using modern tooling (dbt, Airflow, Dagster, Spark).
Experience with entity resolution, deduplication, and fuzzy matching at scale.
Strong understanding of data warehousing (Snowflake, ClickHouse, BigQuery) and performance optimization.
Business-minded: you understand that data quality directly impacts revenue and can articulate tradeoffs in terms the GTM team understands.

Benefits

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Applicant Tracking System Keywords

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Hard Skills & Tools

data engineeringSQLdata modelingentity resolutiondeduplicationfuzzy matchingdata warehousingperformance optimizationproduction data pipelinesenrichment systems

Soft Skills

business-mindedarticulate tradeoffs