Data Analytics Engineer

NetBrain Technologies is a provider of no-code network automation through its Next-Gen platform for hybrid multi-cloud networks. The Senior Analytics Engineer will architect and build the company’s first cloud data warehouse, including transformation layers, ELT pipelines, governance, data quality, and semantic infrastructure for business analytics and internal AI use cases.

Responsibilities:

• 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

Requirements:

• 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