Senior Data Engineer - Healthcare Data & Audience Applications

Zeta Global

1d•$140,000 - $160,000

About The Position

Zeta Global is seeking a Senior Data Engineer to build reliable, scalable data pipelines and data products for a healthcare vertical. You will be a hands-on engineer who turns complex healthcare and marketing datasets into trusted foundations for audience discovery, segmentation, activation, reporting, and measurement. Working closely with the engineering and product team, you will help implement the team's data architecture and engineering standards while owning significant parts of the delivery lifecycle. You will contribute to well-designed, production-ready systems—not define the overall architecture or technical roadmap alone.

Requirements

5–8 years of hands-on data engineering experience, including ownership of production pipelines and data models, with experience working with healthcare data such as provider/HCP, claims, prescription, patient/DTC, or healthcare audience datasets.
Strong Python and expert SQL skills, with demonstrated experience building transformations, optimizing queries, and diagnosing data issues.
Hands-on experience with AWS data services, especially S3, and a modern cloud data warehouse; experience with Snowflake, Airflow, and EMR is strongly preferred.
Experience with data modeling, schema evolution, batch processing, orchestration, testing, CI/CD, and production support practices.
Proven ability to work with large, complex datasets and deliver reliable, well-documented data products.
Deep, practical knowledge of HIPAA, PHI/PII handling, privacy-by-design controls, and the operational requirements of regulated healthcare data environments.
Experience with AdTech/MarTech, identity resolution, audience onboarding, segmentation, data linkage, media measurement, attribution, or campaign reporting.
Ability to balance healthcare privacy constraints with the need for timely, accurate audience and performance insights.
Strong collaboration and communication skills across engineering, product, analytics, and business stakeholders.

Nice To Haves

Experience with healthcare data providers, identity ecosystems, tokenization, clean rooms, or privacy-enhancing technologies.
Experience with data cataloging, lineage, observability, and data-quality frameworks.
Experience with Docker, Kubernetes/EKS, infrastructure as code, and cloud deployment workflows.
Experience supporting reporting, attribution, or measurement products tied to campaign or business outcomes.
Exposure to ML/AI-enabled data products or analytics workflows.

Responsibilities

Design, develop, test, deploy, and operate production-grade pipelines for healthcare, identity, audience, media-exposure, and campaign-performance data using Python, SQL, Airflow, S3, Snowflake, and EMR.
Implement maintainable data models, transformations, governed views, and reusable datasets for provider identity, claims/Rx, NPI/HCP, media, brand, and connector data.
Deliver data products that support HCP and patient/DTC audience discovery, segmentation, activation, measurement, and reporting.
Build Airflow workflows with clear dependencies, retries, alerting, data-quality checks, and operational runbooks; use EMR for large-scale enrichment, normalization, and other compute-intensive workloads.
Write efficient SQL across Snowflake, Hive, and Athena, adapting to platform-specific syntax and query behavior.
Partner with product, analytics, data science, and platform teams to translate business and healthcare requirements into resilient technical solutions.
Implement data-quality controls, reconciliation checks, monitoring, alerting, and incident-response practices for critical data products.
Support data onboarding and integration for healthcare partners and internal sources, including validation, normalization, and source-to-target mapping.
Apply privacy-by-design practices for PHI/PII, including access controls, masking, approved joins, retention, and auditability.
Collaborate with the Lead Data Engineer on technical designs, code reviews, documentation, and delivery plans; mentor less-experienced engineers as needed.
Troubleshoot production issues and improve pipeline performance, reliability, and observability over time.