Senior Data Engineer— AdTech Data Platform

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 . The Role We’re looking for a Senior Data Engineer to design, build, and operate the data processing layer that powers Zeta’s AdTech platform. This is a hands-on role focused on streaming + batch pipelines , producing trusted, reusable aggregates that serve multiple downstream consumers including prediction/ML features, agentic workflows, BI reporting, and measurement . What You’ll Do Build streaming pipelines: Ingest and process high-volume event data using Kafka/Kinesis , handling schema evolution, event-time processing, late data, and deduplication. Create canonical aggregates: Produce durable, well-defined rollups (campaign, audience, creative, inventory, pacing/spend, conversions, measurement) with consistent semantics and SLAs. Enable prediction agents: Deliver feature-ready datasets and near-real-time signals to support model training/scoring, retrieval, and agent decision loops. Support BI reporting: Publish governed datasets to analytics systems and warehouses for dashboards, ad-hoc queries, and operational reporting. Measurement-grade reliability: Implement reconciliation, backfills, audit trails, and quality checks to ensure correctness for reporting and measurement. Optimize performance cost: Tune pipeline throughput/latency, storage formats, partitioning, and compute spend across streaming and batch workloads. Operational excellence observability: Instrument pipelines with metrics/logs/traces, define SLIs/SLOs, and drive fast detection and root-cause analysis. Collaborate cross-functionally: Partner with Backend, ML/DS, Analytics, and Platform/SRE to define contracts, schemas, and robust data products. Required Qualifications 7+ years building and operating production-grade data pipelines. Strong experience with streaming systems : Kafka (preferred) or AWS Kinesis , and event-driven architectures. Hands-on experience with processing frameworks such as Flink , Spark (Structured Streaming) , Beam , or equivalent. Proficiency in Python and/or Java/Scala (Go is a plus). Strong SQL skills and experience with data modeling for analytics and aggregates. Strong experience with AWS and cloud-native data patterns (S3 + compute/orchestration services). Experience with data warehouses / OLAP (e.g., Snowflake/Redshift/BigQuery) and/or real-time analytics stores (e.g., ClickHouse/Druid). Familiarity with SQL + NoSQL ecosystems (e.g., Postgres/MySQL + DynamoDB/Cassandra/Redis) for serving/lookup patterns. Experience with orchestration and CI/CD for data pipelines (Airflow/Argo/Step Functions or equivalents). Clear communicator and collaborator; able to explain data systems and trade-offs to mixed audiences. Preferred Qualifications Programmatic advertising domain knowledge: event pipelines for impressions/clicks/conversions , attribution/measurement, pacing/budget signals. Experience building feature stores or ensuring online/offline parity for ML features. Lakehouse experience (Delta/Iceberg/Hudi), incremental processing, and backfill strategies at scale. Strong data governance practices: li ... (truncated, view full listing at source)