Finance Data Analyst- Analytics & AI

Ensora Health•Remote - Maine, IN

About The Position

Ensora Health is the leading provider of software and services for mental and behavioral health therapists, trusted by over 200,000 individual providers and more than 28,000 practices. Our unmatched expertise, partnership, and breadth of products allow us to fine-tune solutions that meet the specific needs of everyone from solo practitioners to larger practices. With AI-enabled solutions that span practice management to electronic medical records and e-prescribing to billing, we help eliminate administrative complexity and create harmony between therapists, their clients, and the whole healthcare community. We are seeking a Finance Data Analyst who will play a key role in building the reporting foundation our teams depend on today while helping shape the analytics and AI automation capabilities of tomorrow. This role owns financial dashboards, KPIs, and the underlying data models that power them, partnering closely with FP&A, business leaders, and technical teams. This is an opportunity to combine strong finance acumen with modern data tooling and AI-driven workflows to create scalable, reliable, and forward-looking analytics solutions. The ideal candidate is curious about the story behind the numbers, focused on accuracy, and motivated to continuously improve processes through automation and intelligent design.

Requirements

3–5 years of experience in finance analytics, analytics engineering, FP&A analytics, or business intelligence roles
Advanced SQL skills and hands-on experience with dbt and modern data modeling practices
Experience with data visualization tools such as Power BI, Tableau, or similar platforms
Working knowledge of Python for data transformation and automation
Strong understanding of financial concepts including P&L, ARR, revenue, and forecasting processes
Experience supporting or working within financial close and planning cycles
Ability to design scalable solutions that reduce manual effort and improve efficiency
Strong communication skills with ability to translate complex analysis into clear business insights

Nice To Haves

Experience within a SaaS or private equity-backed environment
Familiarity with subscription-based metrics such as ARR, NRR, churn, and bookings
Experience integrating data from systems such as Salesforce or financial planning tools
Exposure to AI/LLM tools or AI-assisted analytics workflows

Responsibilities

Build and maintain finance and operational data models in Snowflake using dbt to support core metrics such as ARR, bookings, revenue, and EBITDA
Own the reliability and scalability of finance-owned data pipelines, including migration of business logic into modern data models
Write and review high-quality SQL and Python code to support data transformation and automation
Develop and maintain financial dashboards and reporting in Power BI or Tableau
Translate business requirements from FP&A and leadership into scalable and production-ready reporting solutions
Define and document KPI logic, metric frameworks, and data dictionaries to ensure consistency and trust in reporting
Perform detailed analysis of financial and operational data to identify trends, drivers, and root causes
Support variance analysis, forecasting processes, and executive-level reporting needs
Communicate insights clearly through structured, narrative-driven summaries for stakeholders
Monitor data quality and ensure integrity of finance datasets within Snowflake
Maintain clear documentation of data lineage, definitions, and reporting standards
Establish governance processes to ensure auditability and alignment across reporting outputs
Identify opportunities to automate recurring finance activities such as close reporting, variance analysis, and forecast updates
Design and implement AI-driven workflows that streamline multi-step finance tasks and reduce manual effort
Enable conversational analytics capabilities that allow stakeholders to query financial data more efficiently
Evaluate and adopt AI tools for forecasting, anomaly detection, and automated reporting
Define governance frameworks to ensure responsible and accurate use of AI-generated outputs
Partner cross-functionally with IT and Data Engineering to support system integrations and data reliability