Remote AI Data Analyst: Data Quality & Growth

Elios, Inc.; United States; Hybrid; USD 70,000 - 95,000; Full time

Job summary

Elios, Inc. in the United States seeks an AI Data Analyst to own analytics assets and data hygiene for the company’s AI applications, in an early‑career, individual‑contributor role within Business Operations at a fully remote insurance and risk‑advisory firm.

You’ll work with operations, finance, and client‑facing teams to build, maintain, and improve analytics views, chase data drift, ensure consistent definitions, and translate fuzzy questions into reliable numbers a business can trust.

Qualifications

• SQL fluency is a must-have and data quality awareness is essential.
• Interest in dbt, warehouse modeling, and pipelines for analytics.
• Experience with BI tools (Tableau/Power BI/Looker) is a plus.

Responsibilities

• Own analytics assets and data hygiene for AI applications.
• Build and maintain analytics views and reporting.
• Spot data drift and chase issues to fix them promptly.
• Protect core definitions so terms are consistent across assets.
• Translate business questions into reliable numbers and insights.
• Gradually extend pipelines and data models, escalating when needed.

Skills

SQL

Data quality

dbt

Warehouse modeling

Pipelines

Python

BI tools

Tools

Tableau

Power BI

Looker

Snowflake

BigQuery

Redshift

Git

Job description

Job Description

AI Data Analyst

Location: Fully remote (US) | Experience: Junior to early-career | Pay: $70,000-$95,000 base

About the Role

You'll own the analytics assets and data hygiene that the company's AI applications depend on, then grow into owning the data models behind them. This is an early-career, individual-contributor role within Business Operations at a fully remote insurance and risk-advisory consulting firm.

When an analytics asset is wrong, whether it's a number that doesn't reconcile or an AI application that quietly returns the wrong answer, people stop trusting it and go back to spreadsheets. This role is there to prevent that. You'll work closely with the teams whose work the numbers describe, learn the modeling and pipeline craft over time, and help keep the data honest.

This team is hiring for curiosity, rigor, sound judgment, and real interest in how the business makes money, not for someone to arrive as an expert. Senior support is there for the genuinely hard parts.

What You'll Own

Be the day-to-day person in the building, working closely with operations, finance, and client-facing teams, driving adoption one person at a time, and earning the trust that gets people to use the system instead of working around it

Own the regular analytics and reporting the firm and its AI applications run on

Build new views when teams need them, and retire the ones nobody trusts

Spot drift early. If a number looks off, you'll chase it down without waiting to be asked and get it fixed.

Protect core definitions so terms like active client mean the same thing across every analytics asset

Turn a fuzzy business question into a number people can believe, and explain what the data can and can't say

Gradually take over the initial pipelines and data models, learning how they work, extending them, and escalating cleanly to the internal lead when something is beyond your scope

What to Look For

The strongest signals here are human ones first.

A real problem-solver, someone with a restless interest in finding the best medium for the business problem rather than attachment to a particular tool

Curiosity about how the business works and makes money

An ownership instinct

Plain, clear communication

Comfort being embedded with teams and acting as an advocate for the work

The ability to build trust

Learning speed and grit

AI engagement is expected, not a bonus. The right candidate should be able to show they've meaningfully used tools like ChatGPT or Claude on their own initiative.

Technical signals matter, but they're coachable. SQL fluency is the one clear must-have, along with a feel for data quality and real eagerness to learn dbt, warehouse modeling, and pipelines.

Nice-to-Haves

Consulting, professional-services, or customer-facing experience

Experience with BI or analytics assets, including Tableau, Power BI, or Looker

Exposure to dbt, a cloud warehouse such as Snowflake, BigQuery, or Redshift, or git

Some Python

Any project where you made messy data trustworthy and useful

Growth Path

This role is designed to grow from AI Data Analyst into Analytics Engineer, taking over pipelines, warehouse models, and the metrics layer, with a compensation step to match.

As the role expands, the emphasis is as much on consulting skill and cross-functional judgment as it is on technical depth.

Compensation

$70,000-$95,000 base, depending on experience and location.

Junior to early-career individual contributor. This role reports into Business Operations.