About us

NinjaOne unifies IT to simplify work for nearly 40,000 customers in 140+ countries

The NinjaOne Unified IT Operations Platform delivers endpoint management, autonomous patching, backup, and remote access in a single console to improve efficiency, increase resilience, and reduce spend. By automating IT and managing all endpoints, organizations give employees a great technology experience at work.

NinjaOne is obsessed with customer success and has retained a 98% customer satisfaction score for more than 5 years.

About the Role

At NinjaOne, we're looking for a skilled Senior Data Engineer to join our Emerging Products team and help build the foundation of our modern data platform. You'll play a critical role in designing and scaling lakehouse architectures, building robust streaming and batch pipelines, and ensuring data flows reliably to the analysts and data scientists who depend on it. This is an exciting opportunity to work on greenfield infrastructure using a cutting-edge stack — Kafka, Spark, Iceberg, and Databricks — collaborate with cross-functional teams, and help shape how we harness data to power our next generation of products.

Location - We are flexible on remote working from home, if you are located in the USA and reside in one of the following states - CA, CO, CT, FL, GA, *IL, KS, MA, MD, ME, NJ, NC, NY, OH, OR, TN, TX, VA, and WA. We have physical offices in Austin, TX and Tampa, FL, if you prefer a hybrid option.

*Onsite interviews may be required for this role.

We hire the best software engineers, but experience in our stack can’t hurt: NinjaOne is built on Java, Kotlin, C++, Golang and Postgres; supporting millions of user endpoints and running as a scalable cloud service in AWS. Knowing large-scale datastore bottlenecks, asynchronous application design and client-server architecture will help you.

What You’ll be Doing

Lakehouse Architecture: Design and implement Medallion-layer data architectures (Bronze, Silver, Gold), building structured ingestion pipelines and curated data layers that serve downstream analytics and data science workloads.
Pipeline Development: Build and maintain scalable streaming and batch data pipelines using Kafka, Spark, and Airflow, supporting reliable and low-latency data movement across the platform.
Table Format Management: Own and manage open table formats including Iceberg, Delta, and Hudi — selecting the right format for the workload and maintaining performance and reliability at scale.
Query Engine Optimization: Work with Trino/Starburst and Databricks-based environments to optimize large-scale analytics queries and support platform consumers.
Platform Reliability: Monitor pipeline health, troubleshoot data quality and performance issues, and continuously improve the reliability and efficiency of the data platform.
Collaboration: Partner closely with data scientists, analysts, and product teams to understand data requirements and deliver platform solutions that enable data-driven decision-making.
Other duties as needed

About You

Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or equivalent work experience preferred.
10+ years of experience in data engineering with a strong focus on data platform and distributed systems.
Hands-on experience building and managing lakehouse architectures, including Medallion (Bronze/Silver/Gold) layering patterns.
Deep expertise in Apache Kafka, Apache Spark, and Apache Airflow for both streaming and batch pipeline development.
Proficiency in open table formats — Iceberg, Delta Lake, or Hudi — with the ability to evaluate trade-offs and select the right format for the workload.
Experience with Databricks for large-scale data processing and analytics workloads.
Strong proficiency in Python and SQL.
Experience that will make you a standout candidate:
Hands-on experience with Trino or Starburst for distributed query execution
Familiarity with hybrid streaming/batch architectures and event-driven data systems.
Experience designing Gold-layer datasets optimized for business intelligence and self-serve analytics.