Data Analytics Engineer

Job Description

Founded in 2004, NetBrain is the leader in no-code network automation. Its ground-breaking Next-Gen platform provides IT operations teams with the ability to scale their hybrid multi-cloud connected networks by automating the processes associated with Diagnostic Troubleshooting, Outage Prevention and Protected Change Management. Today, over 2,500 of the world’s largest enterprises and managed services providers leverage NetBrain’s platform.

What We Need

NetBrain doesn’t have a data warehouse today — different teams work from different numbers, with no standardized reporting layer or single source of truth for the business. We’re hiring a Senior Analytics Engineer to fix that: architecting and standing up our first cloud data warehouse, and building the transformation layer, ELT pipelines, and governance that our analytics function will run on. You’ll own the technical infrastructure that makes reliable measurement possible — warehouse, pipelines, and transformations — while partnering closely with the business stakeholders who define what we measure and why. This is a foundational build with high visibility: the semantic layer you help stand up is also core to how NetBrain uses AI internally to query and report on its own data.

What You'll Do

What You Bring

Nice to Have

What We Offer

Our comprehensive compensation package is vital in how we recognize our people for the impact they make on us reaching our goals as a company.

For this role, the estimated base is $160,000 - $190,000 + Bonus. The actual salary may vary based on a range of factors, including market and individual qualifications objectively assessed during the interview process.

The range listed above is a guideline and may be modified. Speak with your Recruiter for more details on our Total Rewards philosophy.

NetBrain invites all interested and qualified candidates to apply for employment opportunities.

To make a request, please contact our People Team at: people@netbraintech.com and we will be happy to assist you.

In compliance with applicable laws, NetBrain conducts holistic, individual background reviews in support of all hiring decisions.

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

• Architect and maintain NetBrain’s cloud data warehouse,
• Own warehouse transformations end to end — from raw ingestion through documented, stable tables that downstream teams and tools depend on.
• Design and maintain ELT pipelines that bring raw data from tools across GTM, Finance, and Product into the warehouse.
• Contribute to the technical foundation of our BI tool — semantic layer, metric definitions, and access model — that powers executive reporting and NetBrain’s internal AI use cases.
• Own data quality, governance, and lineage: freshness checks, testing, access controls, and a documented source of truth.
• Build and maintain curated data models for product usage, customer adoption, account health, and go-to-market reporting.
• 6+ years in analytics engineering, data engineering, or a similar role, with real ownership of production data models.
• Strong SQL and a track record shipping analytical models that other teams depend on.
• Hands-on experience with a modern cloud data warehouse (Snowflake preferred, or comparable platforms like BigQuery).
• Extensive experience with a transformation framework such as dbt or SQLMesh.
• Scripting ability (Python or similar) for ingestion, validation, or automation.
• Experience with a BI or semantic-layer tool (e.g., Omni, Looker, Tableau).
• Sound judgment around data access, governance, and handling of sensitive data.
• Manual Dexterity: Repetitive motion of wrists, hands and fingers for using a computer.
• Stationary Tasks: Sitting for extended periods, remaining in a stationary position.
• Experience building the technical foundation for AI-assisted or natural-language reporting — semantic layers built to support LLM or agent-based tools.
• Familiarity with GTM and Customer Success data sources such as Salesforce, Gainsight, or HubSpot.
• Experience applying machine learning or statistical forecasting techniques (e.g., time-series and churn-propensity models) to predict key business metrics such as ARR and customer retention.