DataOps Engineer

Core Weave is The Essential Cloud for AI™. Built for pioneers by pioneers, Core Weave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, Core Weave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, Core Weave became a publicly traded company (Nasdaq: CRWV) in March 2025.

We’re proud to be a Living Wage accredited Employer.

The Monolith AI Platform Engineering Team at Core Weave is responsible for building and scaling the data and workflow backbone that powers the world’s most advanced engineering simulation and AI workflows — our ambition is to become the super‑intelligent AI test lab for the engineering industry
, helping customers ship science, faster
. From high‑throughput data ingestion and feature pipelines to model training and real‑time inference, our platform delivers the performant, reliable, and trustworthy data foundation trusted by the world’s largest engineering companies.

The Senior Data Ops Engineer II will own and drive all things data observability and operations across our client estate — building the practices, tooling, and culture that make Monolith’s data flows debuggable, auditable, and safe to evolve. You’ll sit at the intersection of platform engineering, data engineering, and reliability, implementing end‑to‑end lineage and Data Ops practices while mentoring data producers and consumers on how to manage data as a first‑class product.

You’ll partner closely with Monolith’s Product, Engineering and forward‑deployed teams, as well as with Core Weave’s infrastructure and AI platform groups, to turn fragmented, real‑world engineering data into well‑governed, observable, and operationally robust pipelines powering our SaaS platform and client‑specific deployments.

We’re seeking an Senior Data Ops Engineer II who can act as the hands‑on owner for Monolith’s data observability and operational surface: from batch and streaming pipelines running on our platform, through to the lineage, quality, and runbooks that keep customer environments healthy.

You’ll define and roll out Data Ops practices (CI/CD, infra‑as‑code, data SLOs, incident response) across the Monolith estate, implement end‑to‑end data lineage and observability, and serve as the go‑to mentor for engineering teams and client‑facing colleagues on best‑practice data management.

• Own Monolith’s Data Observability & Operations Surface

  • Design and implement the end‑to‑end observability stack for data workloads (metrics, logs, traces, and data‑quality signals) across batch and streaming pipelines.
  • Define and maintain operational SLOs/SLAs for critical data flows powering training, inference, and analytics, and ensure they are measurable and actionable.
  • Build dashboards, alerts, and runbooks that allow engineers and on‑call responders to quickly detect, triage, and remediate data incidents.
  • Standardise “golden paths” for how teams instrument pipelines, expose health signals, and respond to data‑related failures.

• Implement Data Lineage, Quality & Governance

  • Deploy and maintain end‑to‑end data lineage for key domains — from client sources through transformations to features, models, and downstream analytics so teams can debug, audit, and reason about change.
  • Define and roll out data quality checks (schema, freshness, completeness, distribution, drift) and ensure failures integrate cleanly into alerting and incident workflows.
  • Partner with Security, Compliance, and customer‑facing teams to encode data governance requirements (e.g., retention, residency, access controls) into our pipelines and tooling.
  • Help shape metadata models and catalog conventions so that producers and consumers can reliably discover, understand, and use shared datasets.

• Enable Data Ops Practices Across Teams

  • Establish CI/CD patterns for data pipelines and related infrastructure, including testing strategies, promotion workflows, and change‑management guardrails.
  • Drive adoption of infra‑as‑code…