DataOps Engineer

CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave 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, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability.

What You'll Do: The Monolith AI Platform Engineering Team at CoreWeave 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 DataOps 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 DataOps practices while mentoring data producers and consumers on how to manage data as a first‑class product.

In this role, you will:

• 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 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 DataOps 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 for data infrastructure, reducing manual drift across environments. Define and continuously improve DataOps processes — incident response, post‑incident review, change review, on‑call rotations — with a focus on learning rather than blame. Evaluate and integrate best‑of‑breed DataOps and observability tooling where it accelerates our teams.
• Partner Across Monolith, CoreWeave & Clients: Work with Monolith platform, data, agent, and reliability teams to expose observability and lineage as shared services and patterns other engineers can build on. Collaborate with CoreWeave infrastructure and AI platform teams to leverage underlying storage, compute, networking, and observability in service of robust data flows. Serve as a technical escalation point for forward‑deployed and customer‑facing engineers when data issues cross service boundaries or require deeper architectural insight. Mentor data producers and data consumers on resilient schemas, contracts, and operational practices.

Who You Are:

• Experience & Level: Typically 5–6+ years of experience in DataOps, Data Engineering, DevOps/SRE for data platforms, or similar roles, including end‑to‑end ownership of production data pipelines and their operations. Proven track record of operating at Senior IC scope: leading cross‑team initiatives, introducing new practices/tooling, and improving reliability at the platform level.
• DataOps, Pipelines & Tooling: Strong hands‑on experience designing, deploying, and operating data pipelines in production (batch and/or streaming), including failure modes, retries, and backfills. Practical experience with data orchestration and ETL/ELT tooling and comfort evaluating and integrating new tools where appropriate. Solid SQL and/or Spark skills and experience with at least one major analytical database or warehouse.
• Observability, Lineage & Data Quality: Extensive experience implementing data observability — metrics, logging, tracing, dashboards, and alerting — for data‑centric workloads. Hands‑on work with data quality frameworks and/or observability platforms to monitor freshness, completeness, schema changes, and anomalies.
• Platform, Infrastructure & Automation: Comfortable working in containerised, cloud‑native environments; experience with GPU‑or compute‑intensive workloads is a bonus. Strong automation mindset: infra‑as‑code, CI/CD, and configuration management for data infrastructure and observability components. Proficient in Python for building tooling, pipeline glue, and platform integrations.
• Collaboration, Mentorship & Communication: Clear communicator who can explain complex data flows and failure modes to both deeply technical and non‑specialist audiences. Experience mentoring engineers and data practitioners on better data management, observability, and operational hygiene.

Preferred:

• Experience in ML/AI platforms or MLOps environments where data pipelines power experimentation, training, and inference at scale.
• Background with test, simulation, or time‑series data.
• Familiarity with feature stores, experiment tracking, or model registries and their interaction with upstream data pipelines.
• Prior work in multi‑tenant SaaS platforms, especially those with strong compliance, observability, and uptime requirements.

Why CoreWeave? At CoreWeave, we work hard, have fun, and move fast! We're in an exciting stage of hyper‑growth. Our team cares deeply about how we build our product and how we work together, which is represented through our core values:

• Be Curious at Your Core
• Act Like an Owner
• Empower Employees
• Deliver Best‑in‑Class Client Experiences
• Achieve More Together

We support and encourage an entrepreneurial outlook and independent thinking, and foster an environment that encourages collaboration and innovative solutions to complex problems. Come join us!

What We Offer: In addition to a competitive salary, we offer a variety of benefits to support your needs, including family-level medical and dental insurance, generous pension contribution, life assurance, critical illness cover, employee assistance programme, and tuition reimbursement.

Equal Opportunity: CoreWeave is an equal opportunity employer, committed to fostering an inclusive and supportive workplace. All qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, disability, age, sexual orientation, gender identity, national origin, veteran status, or genetic information.