Analytics Engineer

Nuvia Dental Implant Center; Salt Lake City (UT); Remoto; USD 80.000 - 90.000; Tempo pieno

Descrizione del lavoro

Nuvia Dental Implant Center is hiring an Analytics Engineer to elevate the data experience for a core part of our business. You will own the data path from systems to decision, transforming data into clean reporting tables and delivering dashboards used by leadership and field teams.

This embedded role works closely with the business; you’ll optimize data quality, set standards, and partner with a small data engineering team for bigger tasks.

Competenze

• 2+ years of hands-on analytics engineering with visualization experience using SQL and Python to build reporting tables.
• Experience with a BI tool (Looker, Power BI, Tableau, Zoho Analytics, or similar).
• Strong SQL skills including CTEs, window functions, and joins across real-world schemas.

Mansioni

• Treat every data product as your own and ensure it works reliably for users.
• Own what you ship and fix data issues before they are asked.
• Ingest data from source systems and transform it into trusted reporting tables.
• Build and maintain dashboards for leadership and field teams to drive decisions.
• Document metric definitions and dashboard logic to prevent single-person dependency.
• Turn vague stakeholder asks into clear, defensible metrics.
• Collaborate with data engineering on bigger tasks and light data work yourself.
• Triage and resolve data quality issues and ad hoc requests.

Conoscenze

SQL

Python

BI tools

Strumenti

Looker

Power BI

Tableau

Zoho Analytics

Descrizione del lavoro

Role

We're hiring an Analytics Engineer to elevate the data experience for one arm of our business. You'll own the full path from data to decision: get the data from the systems it lives in, transform it into clean and trustworthy reporting tables, and deliver reporting that leadership and the teams on the ground can run this part of the business on. This is an embedded role. You work close to the business, so reporting problems get solved where they start. The core of the job is analytics engineering: modeling, transforming, and visualizing. Some light data engineering will come up along the way (getting a new source connected, fixing a sync). You'll handle it, with a small data engineering team to lean on for anything bigger.

Pay and details

• Pay: $80,000 to $90,000 per year
• Location: Remote within the US. Working hours are 8am to 5pm Mountain Time, Monday through Friday.

Why this role

• Real ownership. The data products you build are yours, from source to dashboard.
• Direct impact. Leadership and the people working in the business use your reporting to run this part of the business, so your work gets seen and used.
• Room to grow. You'll handle light data engineering and can stretch into more engineering work as your skills build.
• A team that learns out loud. We value people who try things, make mistakes, and get better fast.

Responsibilities

• Treat every data product you build as your own. Take pride in it, want it to work every day, and care that it does well for the people who rely on it
• Own what you ship. If a number looks off or a dashboard goes stale, you're the first to notice and the first to fix it, ideally before anyone asks, so the people relying on it always see current, trustworthy data.
• Get data from the source systems that power this part of the business, keep it flowing reliably, and transform it into reporting tables people can trust
• Build and maintain dashboards that leadership and the people working in the business use to make day-to-day decisions
• Set and enforce data standards for your area, and document metric definitions and dashboard logic, so problems get caught at the source and reporting doesn't depend on one person
• Work directly with business stakeholders to turn vague reporting requests into clear, well defined metrics
• Partner with data engineering when a task is bigger than light work like connecting a new source or fixing a sync, and handle that light data engineering yourself
• Triage and resolve data quality issues and ad hoc requests from your stakeholders

Requirements

Must-have

• A real passion for learning. You're willing to try things, make mistakes, fail fast, and grow from it. In our environment, the people who thrive are the ones who keep learning and aren't afraid to be wrong along the way
• 2+ years of hands‑on analytics engineering with some visualization experience, using SQL and Python to build the tables that feed reporting. Experience with a BI tool (Looker, Power BI, Tableau, Zoho Analytics, or similar) helps.
• Strong SQL (CTEs, window functions, joins across messy real-world schemas) and comfort tracking down data quality issues in the source tables
• Working Python (pandas, requests) to pull data from an API or file, clean it, and load it into a database
• Experience transforming raw source data into clean, modeled tables for reporting
• Comfortable working directly with non‑technical stakeholders and turning a vague ask ("why don't these numbers match?") into a clear, defensible metric

Nice-to-have

• Experience scheduling, monitoring, or fixing data pipelines
• Experience embedding with a business team or department
• Familiarity with layered warehouse design (bronze/silver/gold) and dbt‑style conventions
• Experience with a workflow orchestration tool

What Success Looks Like

• First 90 days You understand the business, the source systems, and the key reporting questions for your area You've delivered your first dashboard end to end, and the people it was built for are using it You've found and fixed at least one data quality issue at the source
• First year Leadership and the people working in the business rely on your reporting to run this part of the business Your data is current and trusted, and people stop asking "is this number right?" Your metrics and dashboards are documented so others can pick them up You can move data between systems on your own when needed

Reports To

Reports to the Director of Data. You'll be one of a small group of analytics engineers, each owning reporting for a specific part of the business rather than routing requests through the director. You'll handle light data engineering yourself and lean on a small data engineering team for anything bigger or upstream of your reporting layer. You'll also elevate data quality issues through our existing process instead of building one‑off fixes around it.