Data Analyst

Description

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

- Build the data engine behind a new category of autonomous cloud infrastructure
- High ownership from day one: direct ownership of core data infrastructure and the KPIs the company runs on, with direct exposure to founders
- A small, technical, high-velocity team that values curiosity, speed, rigor, and technical depth over process
- Top-of-market compensation and meaningful equity

Ready to build the data engine behind autonomous AI workloads? Hit Apply.

REQUIREMENTS

What You’ll Bring

- 3–6+ years in Data Analytics, Product Analytics, or Analytics Engineering, including ownership of analysis that drove real product decisions
- 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 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, Airflow/Prefect, or Retool
- Background in cloud infrastructure metrics, compute engines, or MLOps/AIOps telemetry
- B.Sc. in Computer Science, Industrial Engineering, Statistics, or Mathematics, or equivalent
- Experience in an elite IDF technology/intelligence unit (e.g. 8200, Mamram, Matzpen) or another high-performance engineering environment