What You'll Do
Own end-to-end analytics for core product areas: cost optimization, SLA, failure rates, and workload efficiency
Define and instrument key product KPIs and dashboards used by engineering, product, and executives
Build and maintain production-grade data pipelines and models on enterprise-scale telemetry (Databricks/Spark, logs, metrics, traces)
Run deep-dive analyses and experiments to validate features and uncover optimization opportunities
Partner with backend/ML engineers to translate complex multi-cloud performance metrics into actionable platform intelligence
What We Offer
Top-of-market compensation + significant equity (above market standard)
Direct ownership over core data infrastructure and product KPIs
Work with founders, engineering leads, and global enterprise customers on mission-critical systems
Small, high-impact team that values ownership, speed, and technical depth over process
Ready to shape the data engine behind autonomous AI workloads?
Hit Apply.
What You'll Bring
3–6+ years in Data Analytics / Product Analytics / Analytics Engineering (8200 unit experience strongly counts)
Advanced, production-level SQL and strong Python (pandas, numpy; scikit-learn is a plus)
Proven experience with large-scale datasets, scalable data models, and end-to-end data products in production
Strong applied statistics, exploratory data analysis, and technical storytelling to non-technical stakeholders
High ownership mindset and excellent English communication
Nice to Have
Experience with Databricks, Snowflake, Spark, AWS Athena/Glue, dbt, Airflow/Prefect, or Retool
Prior service in an elite IDF technology/intelligence unit (e.g., 8200, Mamram, Matzpen)
B.Sc. in CS / IE / Statistics / Math or equivalent
