RYZE

Analytics Engineer

RYZE United States

Food and Beverage Services · 11-50 employees

Yesterday
Remote data-analyst Mid (2-5 yrs) Full-time United States
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About the role

The Analytics Engineer will own the data infrastructure, including dbt models and Fivetran connectors, to deliver actionable insights for ops and finance stakeholders. They will also contribute to data governance and quality standards while building reliable, business-ready data models in Snowflake.

What they look for

SQL dbt Snowflake Fivetran Data Modeling ELT Pipelines NetSuite ERP Systems Sigma Python R Data Governance Business Intelligence Cohort Analysis Retention Analytics Data Quality

Requirements

Candidates must have 2-5 years of experience in analytics engineering or data science with strong proficiency in SQL and cloud data warehouses like Snowflake. Experience with dbt, ELT pipelines, and ERP systems such as NetSuite is required, along with the ability to translate complex data for non-technical stakeholders.

Full description

COMPANY

ROLE & RESPONSIBILITIES

We’re hiring an Analytics Engineer to join our data team. This is a hybrid role that sits at the intersection of infrastructure and analysis — you’ll own the foundation (dbt models, Fivetran connectors, warehouse hygiene) and use it to deliver insights that ops, finance, and leadership actually act on.

You’re not just a pipeline builder and you’re not just an analyst. You’re the person who does both well — someone who cares as much about whether the model is clean and tested as whether the dashboard answers the right question.

  • Partner directly with ops and finance stakeholders to translate business questions into clean, reliable analyses and Sigma dashboards.
  • Write production-quality SQL for ad hoc analysis, cohort work, and recurring reporting.
  • Build and maintain dbt models in Snowflake that power reporting across the company.
  • Own and monitor a set of Fivetran connectors — keeping pipelines healthy, troubleshooting sync issues, and onboarding new sources.
  • Model NetSuite/ERP data for financial and operational reporting.
  • Contribute to data quality and governance — testing, documentation, and standards that keep our warehouse trustworthy.
  • Flex into ecommerce and retention analytics as team priorities shift — subscription behavior, LTV, channel performance.

PROFESSIONAL QUALIFICATIONS

  • 2–5 years of experience as an Analytics Engineer, Data Analyst, or Data Scientist.
  • Strong SQL skills with the ability to write scalable, efficient, and maintainable queries.
  • Ability to translate complex data into actionable insights for both technical and non-technical stakeholders.
  • Hands-on experience with Snowflake or a comparable cloud data warehouse.
  • Experience building and maintaining data models using dbt.
  • Proven experience managing ELT pipelines and troubleshooting data integration issues (e.g., Fivetran or similar tools).
  • Experience working with large, complex, and imperfect datasets to create reliable, business-ready data models.
  • Strong problem-solving skills with a proactive, ownership-driven mindset and the ability to identify and resolve issues independently.
  • Excellent communication skills with experience presenting findings and recommendations to business stakeholders.
  • Experience working with NetSuite or other ERP systems.
  • Familiarity with the DTC/eCommerce technology stack, including Shopify, Recharge, Klaviyo, Amplitude, or similar platforms.
  • Experience analyzing subscription-based business metrics, including retention, churn, lifetime value (LTV), and cohort analysis.
  • Proficiency in Python or R for advanced data analysis and automation.
  • Experience with business intelligence and visualization tools such as Sigma, Looker, Tableau, or similar (Sigma experience preferred).

PERSONAL CHARACTERISTICS

  • Analytical Thinker – Uses data to solve complex business problems and identify actionable opportunities.
  • Ownership Mentality – Takes initiative, proactively identifies issues, and follows through to resolution without waiting for direction.
  • Business Curious – Seeks to understand the "why" behind the data and partners with stakeholders to drive informed decisions.
  • Problem Solver – Thrives in diagnosing data issues, uncovering root causes, and implementing scalable solutions.
  • Detail-Oriented – Maintains a high level of accuracy while working with complex datasets and reporting.
  • Adaptable – Comfortable shifting priorities and supporting evolving business needs across analytics, finance, and ecommerce.
  • Collaborative Partner – Builds strong relationships with cross-functional teams and effectively translates technical concepts into business language.
  • Continuous Learner – Stays current on analytics best practices, emerging technologies, and data engineering methodologies.
  • Quality-Focused – Committed to maintaining high standards for data accuracy, documentation, governance, and reporting reliability.
  • Accountable – Takes responsibility for deliverables, meets deadlines, and ensures data products are dependable and trusted.

WHAT WE OFFER

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