Analytics Engineer (Data + BI) — Healthcare About FamilyWell Background: FamilyWell Health is an AI-enabled mental health start-up dedicated to solving the women’s mental health crisis by seamlessly embedding high quality, equitable, & affordable mental health care into OB/Gyn practices. FamilyWell provides comprehensive virtual mental health services designed specifically for integration into OB/Gyn practices utilizing the psychiatric collaborative care model. FamilyWell’s virtual care team model delivers evidence-based coaching, therapy, care coordination, and psychiatric services with specialized expertise in perinatal mental health and perimenopause/menopause. About the role Own the end-to-end data stack—from ingestion and warehousing to modeling and BI. You’ll maintain and refine our analytics platform (replica of our Postgres “Registry,” transforms, semantic layer, and dashboards), establish governance for PHI, and enable self-serve analytics across Care Ops, Business Ops, Sales, Partner Success and Revenue Cycle. What you’ll do Platform ownership Maintain a warehouse (Snowflake) and connect it to source systems (EMR, RCM, patient engagement, scheduling, support tools). Implement ELT (currently using Meltano) (plus custom connectors when needed) and orchestration (dbt Cloud/CI). Own company data strategy, detailed architecture and design of replica, warehouse, and BI tool. Modeling & metrics Build curated marts and a governed semantic layer in dbt; define durable metrics (e.g., Time-to-Care, referral funnels, cancellations, provider capacity, cohort outcomes). Add data quality tests (dbt tests/Great Expectations), lineage, and alerts; resolve root causes quickly. BI & enablement Administer BI tool (currently Sigma), define roles/permissions, and ship high-leverage dashboards. Drive stakeholder discovery; translate questions into metrics, dashboards, and data contracts. Train teams on self-serve best practices and documentation. Security & compliance Implement HIPAA-aligned controls: RBAC/ABAC, column-level masking/tokenization, audit logging, data retention, and least-privilege access. Reliability & cost Monitor performance, freshness, and cost (warehouse, ELT, BI); optimize with SLAs for priority datasets.

Qualifications

Must-have 4–7+ years in analytics engineering / data engineering / BI engineering, including end-to-end ownership of ELT→BI. Proficiency with: advanced SQL, dbt (or comparable transformation tooling), a cloud data warehouse (Snowflake preferred; BigQuery/Redshift/Databricks acceptable), and a BI platform (Sigma preferred; Looker/Tableau/Power BI acceptable) Git-based CI/CD, Terraform, Docker. Strong Python skills for light transformations and connector development; dexperience with an EL/ingestion framework (Meltano preferred; Airbyte, Singer SDK, Fivetran, or Stitch acceptable) and an orchestrator (Airflow, Prefect, Dagster, or similar) Experience designing semantic layers (LookML, Metrics Layer, dbt semantic models). Experience integrating healthcare data (EMR/RCM/claim/eligibility/scheduling/patient engagement); working knowledge of HL7/FHIR and healthcare data quirks (encounters, payers, CPT/ICD, denials). HIPAA/PHI practices (de-identification, RBAC, audit logs) and vendor BAA familiarity. Strong stakeholder skills: requirements gathering, translating KPIs, and documentation. Nice-to-have Data reliability tooling (Great Expectations/Monte Carlo/Elementary). Background with CRM/support tools (Salesforce/HubSpot/Zendesk) and marketing/scheduling data. Our stack (target) Sources: Registry (Postgres), Healthie (EMR), Spruce (Patient Messaging), Candid (billing/RCM), Hubspot (CRM), Quickbooks (Accounting). Warehouse: Snowflake BI: Sigma