Data 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 Staff Data Engineer will own and evolve Monolith’s platform data services and ETL offerings — the data onboarding, preparation, and lineage capabilities that turn fragmented, real‑world engineering data into production‑ready training and inference pipelines. You’ll partner with Product, Engineering, and Customer‑facing teams to deeply understand client data challenges and translate them into scalable, self‑serve data platform features.

We’re seeking a Staff Data Engineer who can own Monolith’s data platform surface end‑to‑end: from offline batch pipelines and large historical backfills to low‑latency, real‑time streaming data flows that power online inference and feedback loops. You’ll define and drive our data architecture, champion data quality and lineage, and decide how customer data moves through Monolith from raw ingestion to governed, observable, and reproducible training sets.

You’ll primarily work with internal teams (Product, Customer Success, Forward‑Deployed Engineers, Software Engineers, Data Scientists), and step in as a domain expert when clients need deeper guidance.

• Own Monolith’s Data Platform & ETL Surface
  • Lead the architecture and evolution of core data services for ingestion, transformation, validation, and lineage across training and inference workloads.
  • Design and maintain end‑to‑end data models and schemas that make complex engineering, simulation, and telemetry data discoverable, reusable, and performant.
  • Define standards, contracts, and APIs for how product teams and integrations interact with data services.
• Design & Operate Batch + Streaming Pipelines
  • Build and operate batch pipelines for large‑scale historical imports, retraining data sets, and migrations from legacy environments.
  • Design and implement streaming pipelines (e.g., using Kafka or similar technologies) for event‑driven or real‑time ingestion and transformation that support online inference, monitoring, and feedback loops.
  • Select and integrate off‑the‑shelf, industry‑proven ETL / ELT technologies and own their rollout and long‑term operation.
• Champion Data Lineage, Governance & Data Ops
  • Implement and maintain end‑to‑end data lineage from source systems to derived features and model artifacts, enabling reproducibility, compliance, and faster debugging.
  • Establish Data Ops practices: CI/CD for pipelines, observability (metrics, logs, traces), and operational runbooks for data incidents.
  • Help define data quality and governance standards in partnership with Security, Compliance, and Customer Success, including support for privacy and regulatory needs.
• Partner Across Monolith & Core Weave
  • Work with Monolith product and engineering teams to expose data services that unlock new user workflows and AI capabilities.
  • Collaborate with Core Weave infrastructure and AI platform teams to leverage storage, compute, and observability for reliable data flows.
  • Serve as a technical escalation point for forward‑deployed and customer‑facing engineers when questions go deeper than…