Senior Data Platform Engineer
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At a glance
Job overview
nOps.io is hiring a Senior Data Platform Engineer. Senior Data Platform Engineer at nOps.io, a Series A FinOps platform, will own health, performance, and cost‑efficiency of the Databricks Lakehouse, build and maintain SQL and Python pipelines, manage AWS infrastructure, and partner across engineering, product, and customer success to ensure data accuracy and integrity.
Key focus areas include Own the daily health and performance of our Databricks Lakehouse environment, Build and maintain data pipelines in SQL and Python that transform data into consumable formats, and Optimize legacy jobs and workflows for performance and cost efficiency.
Important skills include Postgres, Google Cloud Platform, Infrastructure, PostgreSQL, Strong Communication, and Curiosity. Preferred (not required): Data Infrastructure, SQL, Finance, and Data Engineering.
Skills & qualifications
Skills
Qualifications
Full job description
About nOps
nOps is a Series A FinOps platform managing over $4B in cloud spend across AWS, Azure, and GCP. We help engineering and finance teams gain real-time cloud cost visibility and achieve autonomous optimization.
The Role
We're looking for a Senior Data Platform Engineer who blends data engineering and data science to help power the next stage of our growth. You'll own the health, performance, and cost-efficiency of our data platform while building the pipelines that drive our product forward. You'll be a key partner across the team — someone who loves digging into how data flows end-to-end, from raw ingestion through to what our customers see in the app, and who takes pride in getting to the root of things.
What You Will Do
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Own the daily health and performance of our Databricks Lakehouse environment (jobs, pipelines, security, workspace cleanup)
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Build and maintain data pipelines in SQL and Python that transform data into consumable formats
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Optimize legacy jobs and workflows for performance and cost efficiency
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Own security administration in Databricks (users, groups, role-based access control)
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Trace data flows end-to-end across the stack — from Databricks through the front-end to what the customer sees
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Partner closely with engineering, customer success, and product to ensure data accuracy and integrity across the platform
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Manage the AWS infrastructure that powers our data environment
What You Will Bring
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5+ years of experience in a hybrid data engineering / data science role
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Deep expertise with Databricks (Lakehouse, pipeline development, optimization)
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Strong proficiency in SQL and Python
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Working knowledge of AWS infrastructure as it relates to data platform operations
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Comfort tracing data issues across the full stack
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Curiosity and a builder mentality — someone who loves spotting inefficiencies and takes ownership of fixing them
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Strong communication skills, especially explaining complex data topics to non-technical stakeholders
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Excitement for a fast-moving Series A environment where you can own outcomes end-to-end
Nice to Have
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2+ years experience with Neon, Lakebase, Supabase, or Aurora Postgres
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Experience with cost optimization at scale on Databricks
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Familiarity with role-based access control (RBAC) and data governance
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Background in FinOps, cloud cost management, or SaaS analytics platforms
Our Stack
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Data: Databricks Lakehouse, Lakebase, PySpark, SQL, Python
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Cloud: AWS
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Front-End / Back-End: Next.js, Vercel
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Data Layer: Postgres
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