Senior Data Engineer

NinjaOne

29d•$110,000 - $200,000•Hybrid

About The Position

At NinjaOne, we’re looking for a skilled Senior Data Engineer to join the Analytics Platform and Data Science team to help enable product analytics and data science across NinjaOne’s expanding product offering. You’ll build the pipelines and data foundations that provide line of sight into product health, adoption, and performance turning raw, messy data into clean, usable datasets that data scientists, analysts, and stakeholders across the Product organization can rely on. This is an exciting opportunity to work at the intersection of business and highly technical products, partner with teams across Product and Engineering, and help shape how we use data to guide product decisions in a fast-paced environment. 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, OR, TN, TX, VA, and WA. We have physical offices in Austin, TX and Tampa, FL, if you prefer a hybrid option.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or equivalent work experience preferred.
7+ years of experience in software development, with a strong focus on data engineering and data science.
Demonstrated business impact and growth in prior roles
Expertise in Python and SQL.
Experience building ETL pipelines and managing large-scale data systems.
Strong experience with Databricks, dbt, and AWS data services; experience with Unity Catalog is a plus.
Comfortable working with structured and unstructured data; experience with product telemetry, event, or streaming data is a plus.
Solid understanding of data cleaning, preparation, and feature engineering to support downstream modeling.
Experience with Tableau or similar BI tools for dashboarding and visualization.
Able to work independently with messy data from sourcing through cleaning to handoff for modeling and comfortable operating in a fast-paced environment.
Strong communicator able to work directly with technical and non-technical stakeholders and bridge business needs with highly technical work.

Nice To Haves

Previous experience partnering directly with data scientists or analytics teams on model-ready data pipelines.
Experience with product analytics usage metrics, adoption and retention analysis, or telemetry/event data at scale.
Understanding of Generative AI and Deep Learning frameworks.
Experience with streaming frameworks (e.g., Kafka, Kinesis, Spark Structured Streaming).

Responsibilities

Drive line of sight into how our products are used and performing by building the data foundation behind product health, adoption, and usage analytics.
Design and build ETL/ELT pipelines with Databricks, dbt, and AWS that extract, transform, and load data from multiple sources, making it usable for analysts and data scientists across the org.
Partner closely with data scientists to prepare structured and unstructured data for modeling — including product usage, adoption, and customer behavior work.
Own the transformation of raw, messy data into clean, well-structured datasets, including feature engineering support (e.g., creating dimensions, discretizing variables).
Build and maintain pipelines for product telemetry, event, and streaming data sources as the team’s needs grow.
Get familiar with NinjaOne’s distributed product architecture and the data it generates, and translate that complexity into clean, well-modeled datasets that bridge business questions with highly technical products.
Automate and optimize data workflows to reduce manual processes and improve efficiency in data collection, storage, and processing.
Build dashboards and reports in Tableau and Databricks to expose key data points and trends to business stakeholders.
Work closely with data science and analytics teammates and with stakeholders and teams across Product and Engineering to gather requirements and ensure clean, usable data flows to the right people.
Other duties as needed.