Posting record
Open 1 day
- ScaleOps' site dates the posting
- Ghostless first sees this listing
- Last checked: still up
How we know
It hasn't been reposted, listed twice, or left up unusually long, and the posting doesn't say it's a talent pool.
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Last checked Nov 4, 2026.
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Upload your resumeWhat it asks of you
- 3+ years of experience
What the posting discloses
Not stated: Pay, real vacancy, AI screening.
Why these three
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Skills mentioned
About the role
ScaleOps is redefining autonomous cloud and AI infrastructure. We're on a mission to free DevOps and platform engineers from manual resource management so they can focus on innovation, not tuning resources. The results: maximized performance and a reduction of cloud costs by up to 80%.
As the category leader in Autonomous Cloud and AI Infrastructure Resource Management, we're trusted by leading enterprises including Adobe, Wiz, Epic Games, Northwestern Mutual, Coinbase, DocuSign, and Fortune 100 companies to autonomously manage their most critical production environments.
Backed by Insight Partners, Lightspeed Venture Partners, and other leading VCs with over $210M in funding, ScaleOps is the leading player in a massive and growing market. We are building the autonomous infrastructure management platform that will power the next decade of enterprise compute.
What You'll Be Doing
We're looking for a Data Engineer to build the data foundation behind ScaleOps' internal AI initiatives and analytics. You'll own pipelines and models spanning infrastructure telemetry and customer conversations, making data from across the business reliable and accessible to AI agents, dashboards, and teams in R&D, Sales, Support, and Customer Success.
- Data Pipelines: Design, build, and operate batch and streaming pipelines that ingest Kubernetes telemetry and data from sources such as Prometheus, Grafana, Salesforce, Gong, and Slack.
- Analytical Data Platform: Own and evolve our ClickHouse data lake, including schema design, partitioning, query performance, coverage, and cost efficiency.
- Data Modeling: Build data models and semantic layers that AI agents and analysts can query reliably.
- Data for AI Applications: Support RAG pipelines, including chunking, embeddings, indexing in vector databases, time series data, and monitoring for freshness and quality.
- Data Reliability: Establish data quality checks, lineage, and observability. Investigate issues and fix their root causes when data breaks.
- Cross-Functional Partnership: Work with AI engineers and business teams to translate their questions into reliable datasets.
What You'll Bring
- Production Data Engineering: 3+ years of experience building and operating data pipelines in production.
- SQL & Python: Strong hands-on skills in SQL and Python.
- Analytical Databases: Experience with a columnar or OLAP database such as ClickHouse, BigQuery, or Snowflake, and with data modeling at scale.
- Pipeline Orchestration: Experience with tools such as Airflow, DBT, or similar.
- Cloud Experience: Hands-on experience working with AWS.
- Ownership & Collaboration: A pragmatic approach, strong ownership, and the ability to work directly with both technical and non-technical stakeholders.
- (Advantage) Experience with Kubernetes, Prometheus, or observability data.
- (Advantage) Experience with vector databases and RAG pipelines.
- (Advantage) Experience integrating CRM or call intelligence data, such as Salesforce and Gong.
- (Advantage) Experience using LLM-based tools in data engineering workflows.
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