Healthcare Data Analyst

aptihealth•Clifton Park, NY

2d•$95,000 - $120,000•Remote

About The Position

Life at aptihealth aptihealth is a fast-growing health tech company transforming behavioral healthcare. Our digital platform and affiliated medical group make it easier for people to access high-quality care when and where they need it. You’ll join a mission-driven team that’s curious, collaborative, and passionate about improving lives through technology and care innovation. Here, ideas turn into action and learning happens every day. We’re proud to be named one of the world’s top health tech companies of 2025, recognized for advancing digital behavioral healthcare. Together, we’re building a better, more accessible future for mental health. What We Offer The chance to make life better for millions of people Smart, supportive colleagues who care about what they do A place where new ideas quickly turn into real results A culture that values curiosity, collaboration, and inclusion A team built on trust, accountability, and continuous learning The Role We are seeking a detail-oriented and collaborative Data Analyst to join our Data Analytics Team. In this role, you will analyze complex data sets to drive platform improvements and support business and clinical decisions. You’ll collaborate with Product, Operations, Growth, and other teams to deliver insights and visualizations that measure impact, evaluate performance, and inform strategy.

Requirements

Bachelor’s degree or higher in a quantitative or informatics field (e.g., Statistics, Mathematics, Economics, Engineering, Computer Science, Information Management).
2+ years of experience in data analysis, business intelligence, or feature engineering.
Advanced experience with Sisense, Power BI, (or similar BI tools)—able to design dashboards for both executive-level decision-making and detailed operational use.
Strong hands-on skills with PostgreSQL (query optimization, schema exploration) and Python (pandas, NumPy, Matplotlib) for automation, transformation logic, and advanced analysis.
Proven ability to independently manage the reporting lifecycle—from stakeholder requirement gathering to schema/data model integration to dashboard delivery.
Ability to collaborate effectively with engineering teams to validate schema changes, integrate new data sources, and maintain consistency across the data model.
Strong data documentation skills: creating and updating data dictionaries, version control of datasets, and maintaining model diagrams with consistent naming and definitions.
Skilled at storytelling with data, delivering insights that highlight trends, comparisons, and business impact—not just raw numbers.
Excellent written and verbal communication skills, with experience presenting to executives and non-technical stakeholders.
Comfortable working in a dynamic environment, responding to shifting priorities and delivering under tight timelines.

Nice To Haves

Experience in healthcare and/or startup technology environments preferred.

Responsibilities

Collaborate with Product, Operations, Clinical, and Finance stakeholders to capture new reporting and analytics needs.
Translate requirements into data needs by defining sources, fields, and transformations.
Partner with Engineering to review schema changes and ensure new or updated data integrates smoothly into the reporting model.
Develop and maintain SQL models (PostgreSQL) across development and production environments—adding, updating, or removing fields, joins, and custom tables as needed.
Apply consistent naming conventions, definitions, and documentation across data models to support flexible and scalable reporting.
Write transformation logic (in Python where needed) to extend or customize data models.
Perform QA and validation of all new or updated fields/tables against source data to ensure accuracy and reliability prior to deployment.
Designing dashboards that translate stakeholder questions into clear, actionable visualizations (charts, KPIs, trends)
Maintaining and updating existing dashboards as data models evolve—removing sensitive information and ensuring PHI/PII is restricted to authorized users.
Optimizing dashboard performance by reducing load times, limiting unnecessary joins, and improving clarity.
Documenting calculations and providing contextual notes and narrative elements so dashboards communicate insights, not just data.
Managing distribution and visibility by scheduling static exports, configuring alerts, and monitoring usage to deprecate or consolidate underutilized dashboards.