Senior Dataops Engineer

Description:

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.

You’ll partner closely with Monolith’s Product, Engineering and forward‑deployed teams, as well as with CoreWeave’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.

About The Role

We’re seeking an Senior DataOps 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 DataOps 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.

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 (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 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 (e.g., pipeline orchestration, storage, observability components), 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, balancing build vs. buy pragmatically.
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 (product teams, integrations, forward‑deployed engineers) and data consumers (data scientists, analysts, client engineers) 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 (e.g., Airflow, Dagster, dbt, Temporal, or similar) 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; familiarity with time‑series / telemetry data is a plus.
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.
Experience deploying and using data lineage or metadata/catalog solutions, and applying them to debugging, compliance, and change‑impact analysis.
Platform, Infrastructure & Automation
Comfortable working in containerised, cloud‑native environments (Kubernetes plus at least one major cloud provider); 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; additional languages are a plus.
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 — through documentation, examples, reviews, and office hours.
Comfortable working in a fast‑moving, high‑ambiguity environment where we balance rapid iteration with the safety and reliability demanded by enterprise engineering clients.