Lead Data Engineer - Healthcare Data & Audience Applications

Role details

WHO WE ARE Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently. Through the Zeta Marketing Platform (ZMP), our vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform – powered by one of the industry’s largest proprietary databases and AI. Our enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs. Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world. To learn more, go to www.zetaglobal.com . ROLE OVERVIEW Zeta Global is seeking a Lead Data Engineer to build and scale healthcare-focused applications and data systems that power audience intelligence, activation, measurement, and reporting across both HCP and DTC workflows. This is a hands-on technical leadership role operating at the intersection of distributed systems, data engineering, and healthcare domain constraints. You will design and deliver systems that integrate identity, audience data, and campaign performance while meeting strict requirements for privacy, compliance, and reliability. The ideal candidate brings strong system design depth, experience building data-intensive platforms, and the ability to lead through architecture and execution in regulated environments. What You’ll Build Applications supporting HCP and patient / direct-to-consumer (DTC) audience discovery, segmentation, and activation Scalable data pipelines for ingestion, normalization, and enrichment of healthcare datasets Identity resolution and data linkage services across fragmented data sources Reporting, attribution, and measurement systems connecting campaigns to outcomes APIs and services enabling downstream activation, analytics, and partner integrations Healthcare Cloud and data-mapping architecture across claims/Rx, NPI/HCP, patient, media-exposure, brand, and connector datasets. Healthcare-specific semantic cubes, governed views, data contracts, and domain APIs consumed by Vertical Apps; front-end applications should consume stable domain APIs rather than query raw stores or Cube directly. Identity resolution, match-rate measurement, medical-code mapping, and approved joins under CERT/HIPAA rules. Foundational data products for NPI onboarding, PLD reporting, script reporting, HCP/Patient intelligence, journeys, and Next Best Action. Key Responsibilities Define and maintain the healthcare data architecture, interface contracts, ADRs, schemas, and staged delivery plan. Lead ingestion and transformation patterns across Snowflake, Athena/S3, MySQL, and approved healthcare stores without duplicating shared platform capabilities. Partner with Data Cloud on source certification, partner-feed contracts, identity validation, raw-zone ownership, and data availability SLAs. Design healthcare base/vertical models and published/governed views, with row/member-level controls, account scoping, masking, and backward-compatible versioning. Set SLOs and operational readiness criteria; ensure dashboards, alerts, runbooks, rollback paths, and incident ownership are in place before release. Lead design and code reviews, mentor the Senior Data Engineer, and coordinate dependencies across the pod. Support compliance evidence, annual audits, US data-residency controls, and stoplight decisions: Start, Stop, or Requires CERT. Apply privacy-by-design principles across all systems handling PHI/PII Partner with Product and Data teams to translate healthcare requirements into scalable architectures Drive engineering best practices across testing, CI/CD, code quality, and operational excellence Engineering Expectations Strong experience designing distributed systems, including microservices and event-driven architectures Deep understanding of data modeling, storage systems (OLTP and OLAP), and data-processing frameworks Experience with streaming and batch processing technologies, such as Kafka, Spark, Flink, or similar tools Proficiency in backend development using Python, Java, or Ruby, as well as API design using REST or gRPC Experience with workflow orchestration tools, such as Apache Airflow Experience working with data warehouses and lakehouse platforms, such as Snowflake, Databricks, or similar technologies Strong experience with cloud platforms (AWS, GCP, or Azure) and infrastructure as code Familiarity with containerization and orchestration technologies, including Docker and Kubernetes Experience implementing observability practices, including logging, metrics, and tracing, as well as reliability patterns Strong understanding of performance optimization and scalability trade-offs Healthcare & Domain Expectations Experience working with HCP data (provider identity, targeting, segmentation) Experience with patient / DTC data workflows and privacy-aware systems Understanding of healthcare data ecosystems (claims, provider, or audience datasets) Familiarity with identity graphs and data onboarding patterns Experience building reporting or attribution systems tied to business outcomes Strong grasp of HIPAA, PHI/PII, and regulated data handling requirements Technical standards for lineage, data quality, access control, masking, retention, auditability, observability, and cost. Qualifications 8+ years of data engineering experience with increasing technical ownership Expert SQL and strong Python, Java; experience with batch and streaming patterns, orchestration, testing, and schema evolution. Proven track record of building and scaling data-intensive or distributed systems Experience working in regulated environments (healthcare strongly preferred) Strong system design and architecture skills Ability to le