Senior Data Engineer

Zeta Global•San Francisco, CA

About The Position

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. We’re looking for a Senior Data Engineer to design, build, and operate the data pipelines and aggregates that power Zeta’s AdTech platform. This is a hands-on individual contributor role focused on high-scale batch + streaming data processing, reliable data products, and analytics-ready datasets that enable prediction, agentic workflows, BI reporting, and measurement. You will partner closely with backend, ML, and product teams to deliver trusted, well-modeled data with strong performance, quality, and observability.

Requirements

5+ years building production data pipelines and data products (batch and/or streaming) in a high-scale environment.
Strong experience with SQL and data modeling (dimensional modeling, star/snowflake schemas, event modeling).
Hands-on experience with streaming systems (Kafka preferred) and/or AWS Kinesis, including event-driven designs.
Proficiency in one or more languages used for data engineering (Python, Java, Scala, or Go).
Experience with distributed data processing (Spark, Flink, or equivalent) and performance tuning at scale.
Experience with AWS data services and cloud-native patterns (S3, Glue/EMR, Athena, Redshift, etc. as applicable).
Familiarity with lakehouse/table formats and large-scale storage patterns (e.g., Parquet; Iceberg/Hudi/Delta are a plus).
Experience with orchestration/workflow tooling (Airflow/Dagster/Step Functions) and CI/CD for data workloads.
Strong data quality/observability practices (tests, monitoring, lineage; understanding of SLAs/SLOs).
Experience with SQL + NoSQL data stores (e.g., Postgres/MySQL; DynamoDB/Cassandra/Redis) and choosing the right store per use case.
Clear communicator and collaborator; able to work with mixed audiences and translate needs into reliable data interfaces.

Nice To Haves

AdTech / programmatic advertising domain knowledge: DSP/SSP/exchange/RTB concepts and data flows.
Experience building measurement pipelines (attribution, incrementality, lift, or experimentation analytics).
Experience supporting ML feature stores, offline/online feature generation, or model training datasets.
Experience with real-time analytics stores (Druid/ClickHouse/Pinot) and high-cardinality aggregation strategies.
Deep knowledge of data governance/privacy, including PII handling and consent-aware data processing.
Open-source contributions, publications, or conference speaking.
BS/MS in CS/Engineering or equivalent practical experience.

Responsibilities

Build data pipelines: Develop robust batch and streaming pipelines (Kafka/Kinesis) to ingest, transform, and enrich large-scale event data (impressions, clicks, conversions, costs, identity signals).
Create data aggregates & marts: Design and maintain curated aggregates and dimensional models for multiple consumers—prediction models, agents, BI dashboards, and measurement workflows.
Data modeling & contracts: Define schemas, data contracts, and versioning strategies to keep downstream systems stable as sources evolve.
Data quality & reliability: Implement validation, anomaly detection, backfills, and reconciliation to ensure completeness, correctness, and timeliness (SLAs/SLOs).
Performance & cost optimization: Optimize compute/storage for scale (partitioning, file sizing, incremental processing, indexing), balancing latency, throughput, and cost.
Orchestration & automation: Build repeatable workflows with scheduling/orchestration (e.g., Airflow, Dagster, Step Functions) and CI/CD for data pipelines.
Observability for data systems: Instrument pipelines with metrics, logs, lineage, and alerting to accelerate detection and root-cause analysis of data issues.
Security & governance: Apply least-privilege access, PII-aware handling, and governance controls aligned with enterprise standards.