Senior Analytics Engineer – Data, BI

FamilyWell Health

Senior Analytics Engineer owning end-to-end data stack, focusing on BI integration for mental health AI startup. Collaborating across teams to implement analytics and ensure data quality and compliance.

Posted 6/19/2026full-timeRemote • 🇺🇸 United StatesSeniorWebsite

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Hard Skills

advanced SQLdbtSnowflakeMeltanoPythonGitTerraformDockerBI toolsdata quality tests

Soft Skills

stakeholder discoveryrequirements gatheringdocumentationtraining teamscommunication

Tools & Technologies

SigmaEMRRCMAirflowPrefectDagsterGreat Expectationsdata warehousecloud data warehouseorchestration

Certifications & Qualifications

HIPAA compliancePHI practices

Industry Keywords

healthcare dataHL7FHIRde-identificationdata retentionleast-privilege accessmetricssemantic layerdata contractsELT

Tech Stack

Tools & technologies

AirflowAmazon RedshiftBigQueryCloudDockerPythonSQLTableauTerraform

About the role

Key responsibilities & impact

Own the end-to-end data stack—from ingestion and warehousing to modeling and BI.
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.
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.
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.
Implement HIPAA-aligned controls: RBAC/ABAC, column-level masking/tokenization, audit logging, data retention, and least-privilege access.
Monitor performance, freshness, and cost (warehouse, ELT, BI); optimize with SLAs for priority datasets.

Requirements

What you’ll need

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.

Benefits

Comp & perks

Health insurance
Flexible work arrangements