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 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.

About the Role:

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.

In this role, you will:

• 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 (e.g., workflow orchestrators, ingestion frameworks, transformation engines) and own their rollout and long‑term operation.
• Champion Data Lineage, Governance & DataOps
• Implement and maintain end‑to‑end data lineage from source systems to derived features and model artifacts, enabling reproducibility, compliance, and faster debugging.
• Establish DataOps 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 (e.g., GDPR‑aligned flows and data residency discussions).
• Partner Across Monolith & CoreWeave
• Work with Monolith product and engineering teams (platform, agents, experiments, reliability) to expose data services that unlock new user workflows and AI capabilities.
• Collaborate with CoreWeave 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 their playbooks (e.g., architecture diagrams of data flow, lineage, and governance constraints).

Who You Are:

• Experience & Level
• Typically 8+ years of experience as a Data Engineer / Data Platform Engineer (or similar), including ownership of production data pipelines and architectural decisions.
• Demonstrated Staff‑level impact: leading critical data domains and cross‑team initiatives.
• Data Engineering & Architecture
• Deep experience designing end‑to‑end data architectures that cover ingestion, storage, transformation, serving, and observability.
• Strong, hands‑on experience with both batch and streaming pipelines:
• Batch: scheduled workloads, historical backfills, retraining pipelines.
• Streaming: Kafka or similar event streaming platforms plus real‑time transformation patterns for low‑latency consumption.
• Proficiency with SQL and at least one major analytical database or data warehouse (e.g., PostgreSQL or similar) including schema design and performance tuning
• Proficiency with Spark, Ray or similar distributed data processing frameworks.
• Solid understanding of data modeling (event logs, star schemas, feature tables) in multi‑tenant SaaS or platform contexts.
• Tooling & Ecosystem
• Hands‑on with data orchestration and ETL tooling (e.g., Airflow, dbt, Dagster, Temporal, or equivalents) and able to evaluate and recommend tools that fit our needs.
• Experience integrating and operating off‑the‑shelf data infrastructure (not just bespoke systems), including rollout, migration plans, and ongoing ownership.
• Familiarity with cloud infrastructure and containerization (Docker, Kubernetes, at least one major cloud provider) for deploying and scaling data workloads.
• Data Lineage, Quality & DataOps
• Extensive experience implementing data lineage solutions and using them for debugging, compliance, and auditability.
• Strong background in data quality: validation frameworks, monitoring, and guardrails that prevent bad data from reaching downstream consumers.
• Proficiency with DevOps / DataOps practices: infra‑as‑code, CI/CD for pipelines, runbooks, and participation in on‑call / incident response for data issues.
• Programming, Systems & Communication
• Strong programming skills in Python for building data services, transformations, and platform integrations, with an emphasis on maintainability and tests.
• Comfortable working in service‑oriented architectures, reasoning about data contracts, SLAs, and failure modes across services.
• Clear written and verbal communicator who can:
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