Business Intelligence Engineer

AnswersNow Inc•,

1d•$130,500 - $152,250•Remote

About The Position

Business Intelligence Engineer Remote, Anywhere in the US About AnswersNow At AnswersNow, we are trailblazing the future of autism therapy, making it more immediate, accessible, and effective for families everywhere. Our innovative virtual ABA therapy platform is thoughtfully designed by clinicians to recreate the focused, supportive environment of in-person therapy, complete with distraction-free features and interactive activities that enhance engagement and progress. Our team operates fully remote—meaning you’ll have the flexibility to work from the comfort of home. If you're ready to make a meaningful impact and join a team that's reshaping autism therapy, we’d love to hear from you! Why this role matters: As our Business Intelligence Engineer, you are the ultimate architect of our data and the bridge to our AI-driven future. You’ll clear out the noise to create a single, trusted source of truth that powers critical business decisions and fuels our homegrown AI tools. By building a rock-solid data foundation, you ensure our internal teams, external partners, and digital systems always have accurate, reliable insights. Ultimately, your work empowers us to scale our platform and deliver life-changing autism therapy to families nationwide. Job Details W2 Employee 100% Remote

Requirements

4+ years of experience in data engineering, analytics engineering, or BI architecture
Deep SQL proficiency — you write complex queries fluently and think in terms of data models, not just ad hoc queries
Strong experience with dbt: building models, writing tests, managing the DAG, and thinking in dimensions and facts
Hands-on Amazon QuickSight experience — or deep experience with a comparable BI platform and a demonstrated ability to learn new tooling quickly
You think like a data modeler: you consider how a design decision today affects the flexibility and trustworthiness of reports built on top of it tomorrow
Familiarity with LLM-assisted BI tools, AI-native data products, or QuickSight AI features — you're not afraid of this space and ideally excited by it
Strong communication skills: you can explain a data model decision to a non-technical stakeholder without making them feel lost
Ability to work closely with business stakeholders to understand domain context — you can't model data well if you don't understand what the data means

Nice To Haves

Experience with clinical data, healthcare outcomes metrics, or payor reporting
Familiarity with RAG pipelines, vector databases, or AI inference infrastructure — awareness of how BI data feeds AI systems
Background in a high-compliance data environment (HIPAA, SOC 2)
Experience migrating from one BI platform to another (Mode, Looker, Tableau, or similar)

Responsibilities

Design and maintain dbt models that encode business logic, metric definitions, and dimensional structures — the foundation that all BI reports are built on
Enforce consistent metric definitions across the semantic layer so that every downstream report reflects the same agreed-upon numbers
Work closely with the Senior Manager of Data to translate the definitions alignment work into concrete dbt model implementations
Document data models clearly enough that analysts and business stakeholders can understand what each model contains and how to use it
Own the AnswersNow QuickSight environment end-to-end: data source connections, SPICE datasets, refresh schedules, access controls, and performance
Architect the QuickSight layer for the five-domain dashboard buildout — designing for maintainability, performance, and consistent user experience
Lead the technical execution of the Mode-to-QuickSight migration, working with the Senior Manager of Data on sequencing and stakeholder communication
Configure and leverage QuickSight AI features (QuickSight Q, AI-assisted narratives) to extend the value of dashboards beyond static charts
Own the data quality and model accuracy that the our homegrown analytics agent depends on — if the agent gives a wrong answer, the root cause often lives in the data layer
Build validation checks, data quality tests, and monitoring that surface issues before they reach the agent or downstream users
Partner with the Data Science team on where the BI layer intersects with AI inference pipelines
Design and maintain outcomes reporting packages for external payor audiences, including clinical outcome metrics, utilization summaries, and quality indicators
Ensure payor-facing reports meet accuracy standards — these reports carry external contractual and reputational weight
Work with clinical and business stakeholders to define the metrics and formats that payor partners require
Stay current on LLM-assisted BI tools and evaluate where they can reduce the burden of report creation, metric explanation, or ad hoc analysis
Apply AI-native thinking to BI infrastructure — designing datasets and models that are well-suited to be queried by both humans and AI agents
Contribute to the team's understanding of where AI can accelerate BI delivery and where human judgment is irreplaceable