Senior Data Engineer, Zeta Global

• 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

Desirable

• 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
• 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