Senior Data Platform Engineer

nOps; Northern (KY); Hybrid; USD 140,000 - 200,000; Full time

Job summary

nOps is seeking a Senior Data Platform Engineer to own the health, performance, and cost-efficiency of our data platform. You’ll build end-to-end data pipelines from ingestion to customer-facing dashboards and work across engineering, finance, and product to drive data accuracy and reliability.

Bring 5+ years in a hybrid data engineering/data science role with deep Databricks expertise, strong SQL and Python skills, and familiarity with AWS data infrastructure.

Qualifications

• 5+ years in hybrid data engineering / data science
• Deep Databricks expertise (Lakehouse, pipelines, optimization)
• Strong SQL and Python proficiency
• Working knowledge of AWS data infrastructure
• Ability to trace data issues across the full stack
• Curiosity with a builder mindset and ownership to fix inefficiencies
• Strong communication skills for non-technical stakeholders
• Excitement for a fast-moving Series A environment with end-to-end ownership

Responsibilities

• Own daily health and performance of Databricks Lakehouse, jobs, pipelines, security, and workspace cleanup
• Build and maintain data pipelines in SQL and Python transforming data into consumable formats
• Optimize legacy jobs and workflows for performance and cost efficiency
• Own security administration in Databricks (users, groups, RBAC)
• Trace data flows end-to-end from Databricks to front-end to customer view
• Partner with engineering, customer success, and product to ensure data accuracy and integrity
• Manage the AWS infrastructure powering the data environment

Skills

Databricks

SQL

Python

AWS

Tools

Databricks Lakehouse

Neon

Lakebase

Supabase

Aurora Postgres

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

• Own the daily health and performance of our Databricks Lakehouse environment (jobs, pipelines, security, workspace cleanup)
• Build and maintain data pipelines in SQL and Python that transform data into consumable formats
• Optimize legacy jobs and workflows for performance and cost efficiency
• Own security administration in Databricks (users, groups, role-based access control)
• Trace data flows end-to-end across the stack - from Databricks through the front-end to what the customer sees
• Partner closely with engineering, customer success, and product to ensure data accuracy and integrity across the platform
• Manage the AWS infrastructure that powers our data environment

What You Will Bring

• 5+ years of experience in a hybrid data engineering / data science role
• Deep expertise with Databricks (Lakehouse, pipeline development, optimization)
• Strong proficiency in SQL and Python
• Working knowledge of AWS infrastructure as it relates to data platform operations
• Comfort tracing data issues across the full stack
• Curiosity and a builder mentality - someone who loves spotting inefficiencies and takes ownership of fixing them
• Strong communication skills, especially explaining complex data topics to non-technical stakeholders
• Excitement for a fast-moving Series A environment where you can own outcomes end-to-end

Nice to Have

• 2+ years experience with Neon, Lakebase, Supabase, or Aurora Postgres
• Experience with cost optimization at scale on Databricks
• Familiarity with role-based access control (RBAC) and data governance
• Background in FinOps, cloud cost management, or SaaS analytics platforms

Our Stack

• Data: Databricks Lakehouse, Lakebase, PySpark, SQL, Python
• Cloud: AWS
• Front-End / Back-End: Next.js, Vercel
• Data Layer: Postgres