AI Data Analytics Engineer

Elios AI is a professional-services and consulting business focused on enhancing its data-to-AI layer. They are seeking an AI Data Analytics Engineer to manage analytics assets and ensure data integrity for their AI applications, facilitating collaboration across teams to build trust in the data.

Responsibilities:

• Be the in-house face of the data layer, sitting with teams day to day and driving adoption one person at a time by showing them how an analytics asset answers their actual question
• Own the recurring analytics and reporting the business and its AI applications depend on, building new views when a team needs them and retiring the ones nobody trusts
• Catch drift before anyone else does when a source system changes, a definition shifts, or a feed breaks, then flag it, explain it plainly, and get it fixed
• Protect the definitions so 'active client' doesn't mean three different things to three teams, and keep those definitions consistent across every asset
• Turn a fuzzy question from a team lead ('are we actually getting faster on intake?') into a number people believe, and explain what the data can and can't tell them
• Gradually take over the models: learn the pipelines and warehouse layer, extend them yourself, and escalate cleanly to your internal lead when something is genuinely hard

Requirements:

• 1 to 3+ years of experience
• SQL fluency: comfortable writing queries, joining tables, and reasoning about data
• A feel for data quality, ideally having caught a bad number before and knowing how messy real data gets
• Eagerness to learn the modeling craft (dbt, warehouse modeling, how pipelines fit together)
• A restless problem-solver who reaches for whatever tool cracks the business problem fastest
• Genuine curiosity about how a business runs and which numbers matter to which team
• An ownership instinct that treats a wrong number as your problem to chase down
• The ability to explain a finding to a non-technical manager without jargon
• Comfort being embedded with a team and being the person others come to with their data questions
• You've meaningfully leaned into AI on your own initiative, using tools like ChatGPT or Claude
• a consulting, professional-services, or customer-facing background
• hands-on time with Tableau, Power BI, or Looker
• exposure to a cloud warehouse (Snowflake, BigQuery, Redshift) or git
• a little Python