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Responsibilities
Build and operate scalable data platforms and shared data products that power Zillow’s next generation of AI experiences
Own and evolve trace and event data foundations that support analytics, online and offline evaluation, observability, governance, and future learning workflows
Design and operate production-grade batch, streaming, and near-real-time data pipelines using Databricks, Spark, Python, SQL, and modern lakehouse patterns
Improve platform reliability through schema management, data quality validation, alerting, anomaly detection, and production support for business-critical AI data products
Partner with engineering, analytics, and science teams to turn evolving AI data needs into reusable infrastructure and governed data products
Help define the long-term architecture for AI data systems, including data contracts, eventing patterns, retention approaches, and cleaner downstream interfaces
Requirements
5+ years of experience in big data engineering, data platform engineering, machine learning engineering, or a closely related field
Strong proficiency in Python and SQL
Experience building production-grade data systems on distributed platforms such as Databricks and Spark
Designed and operated scalable batch, streaming, or near-real-time pipelines and datasets for analytics, observability, and AI or ML use cases
Comfortable working with complex, high-volume event, telemetry, trace, or log-style datasets
Improved production reliability through schema evolution, ingestion safeguards, monitoring, alerting, root-cause analysis, and operational ownership
Experience with technologies and practices such as Kafka, Spark Structured Streaming, MLflow, OpenTelemetry, CI/CD, and Git-based workflows
Bereit?
Bewerbung für Jobtailor fortsetzen · kein Konto nötig.
