Lead Data Engineer - Healthcare Data & Audience Applications

Zeta Global

11d•$180,000 - $200,000

About The Position

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.

Requirements

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.
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.
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 lead technically while remaining hands-on in implementation.
Strong collaboration skills across engineering, product, and data teams.

Nice To Haves

Experience with healthcare data providers or identity ecosystems.
Background in AdTech, MarTech, or audience/data platforms.
Experience with privacy-enhancing technologies (tokenization, clean rooms).
Exposure to ML/AI-driven data products or analytics systems.

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.