About this role
Accountabilities:
- Architect, build, and maintain a modern cloud data warehouse that serves as the foundation for enterprise analytics.
- Own data transformations end to end, from raw ingestion through reliable, documented, and stable analytical tables.
- Design and maintain ELT pipelines that integrate data from GTM, Finance, Product, Customer Success, and other business systems.
- Establish the technical foundation for BI and semantic-layer capabilities, including metric definitions, data models, and access structures.
- Develop curated data models supporting product usage, customer adoption, account health, and go-to-market reporting.
- Own data quality, governance, and lineage through freshness monitoring, automated testing, access controls, documentation, and source-of-truth standards.
- Partner closely with business stakeholders to understand measurement requirements and translate them into scalable analytical solutions.
- Help establish data infrastructure that can support internal AI-powered querying, reporting, and natural-language analytics.
Requirements:
- 6+ years of experience in analytics engineering, data engineering, or a related field, with demonstrated ownership of production data models.
- Strong SQL expertise and a track record of delivering analytical models relied upon by multiple teams.
- Hands-on experience with modern cloud data warehouses, with Snowflake preferred or comparable platforms such as BigQuery.
- Extensive experience with a transformation framework such as dbt or SQLMesh.
- Proficiency in Python or a similar scripting language for data ingestion, validation, automation, or related engineering tasks.
- Experience working with BI or semantic-layer platforms such as Omni, Looker, Tableau, or comparable tools.
- Strong judgment around data governance, access controls, data quality, lineage, and the responsible handling of sensitive information.
- Experience supporting AI-assisted or natural-language reporting and semantic layers designed for LLM or agent-based applications is a plus.
- Familiarity with GTM and Customer Success platforms such as Salesforce, Gainsight, or HubSpot is advantageous.
- Experience with machine learning or statistical forecasting, including time-series or churn-propensity models for metrics such as ARR and customer retention, is a plus.
- Ability to perform repetitive computer-based tasks involving wrists, hands, and fingers, and to remain seated or stationary for extended periods.
Benefits:
- Estimated base salary of $160,000–$190,000 plus bonus.
- Comprehensive benefits package in addition to cash compensation.
- 401(k) plan.
- Medical and dental coverage.
- Total Rewards package with additional benefits based on eligibility.
- Compensation may vary depending on market considerations and objectively assessed individual qualifications.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Why Apply Through Jobgether?
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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