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Zipher is building the Autonomous Execution Layer for cloud data and AI workloads. Backed by $50M in funding, we dynamically orchestrate clusters, predict bottlenecks, and auto-heal infrastructure in real time — with zero human intervention. Our platform runs in production at global enterprise customers, including Fortune 500 companies, delivering mission-critical resilience and sub-second optimization.
We are looking for a Data Analyst to own the intelligence layer on top of Zipher’s autonomous execution engine. You will turn enterprise-scale telemetry from production data and AI workloads into the metrics, models, and answers that engineering, product, and executives run on.
What you'll do
Own end-to-end analytics for core product areas: cost optimization, SLA, failure rates, and workload efficiency
Define and instrument the product KPIs and dashboards used daily by engineering, product, and the executive team
Build and maintain production-grade data pipelines and models on enterprise-scale telemetry — Databricks/Spark, logs, metrics, and traces
Partner closely with backend and ML engineers to translate complex multi-cloud performance data into actionable platform intelligence
Run deep-dive analyses and experiments that validate features, quantify impact, and uncover new optimization opportunities
What We Offer
A chance to build the core execution engine for a new category of autonomous data platform
High ownership from day one: real architectural influence, direct exposure to founders, and responsibility for mission-critical systems
Technical, high-velocity team that values curiosity, speed, and engineering craftsmanship
Top-of-market compensation and meaningful equity
Ready to build the data engine behind autonomous AI workloads?
Hit Apply.
Requirements
What You’ll Bring
Experience in an elite IDF technology/intelligence unit (e.g. 8200, Mamram, Matzpen)
3–6+ years in Data Analytics, Product Analytics, or Analytics Engineering, including ownership of analysis that drove real product decisions
Proven experience with large-scale datasets, scalable data models, and end-to-end data products running in production
Strong applied statistics and exploratory analysis, plus the technical storytelling to make findings land with non-technical stakeholders
A high-agency, engineering-first mindset: you enjoy ambiguous, high-leverage problems and take responsibility for the correctness and quality of what you ship
Nice to have
Experience with Databricks, Snowflake, Spark, AWS Athena/Glue, dbt, or Airflow/Prefect
Background in cloud infrastructure metrics, compute engines, or MLOps/AIOps telemetry
B.Sc. in Computer Science, Industrial Engineering, Statistics, or Mathematics, or equivalent
Advanced, production-level SQL and strong Python (pandas, numpy; scikit-learn is