Our Client is hiring a Full‑Stack Data Engineer to strengthen their data foundation and support growing reporting needs across the organization. This is a hands‑on technical role for someone who thrives across the entire data lifecycle from building pipelines and transformations to delivering user‑facing dashboards and predictive insights.
You will join a collaborative data team, working closely with engineering and analytics colleagues to ensure reliable data ingestion, efficient workflows, and clear reporting outputs that empower operational and clinical leadership.
Requirements
Key Responsibilities
Data Engineering
- Design, build, and maintain data pipelines using GCP tools (BigQuery, Cloud Functions, Cloud Composer, Cloud Scheduler, Apache Beam, Airflow).
- Clean, transform, and organize data from multiple sources.
- Automate ETL/ELT workflows for reliability and scalability.
- Support ingestion from APIs, spreadsheets, and internal systems.
Backend Development
- Write Python and Bash scripts to process and automate data tasks.
- Develop lightweight backend services and utilities to streamline internal processes.
Front‑End / Dashboards
- Build and update dashboards in Looker Studio and D3.js.
- Deliver clean, intuitive KPI reports for operations and leadership.
- Support visualization needs across the Wellness Division.
Foundational ML / Predictive Work
- Contribute to simple predictive modeling and forecasting tasks.
- Prepare structured datasets for future machine learning initiatives.
Qualifications
Must‑Have:
- 2+ years in data engineering, data science, or software engineering.
Strong experience with GCP, including:
- BigQuery
- Cloud Functions / Cloud Run
- Apache Beam & Airflow
- Looker / Looker Studio Pro
- Vertex AI (AutoML, LLM engineering)
- Advanced Python (data processing, APIs, automation).
- Experience building end‑to‑end pipelines (batch + streaming preferred).
- Strong SQL skills for transformations and modeling.
- Proven ability to develop dashboards, KPIs, and BI outputs.
- Solid understanding of modern data architectures (lakehouse, warehousing, governance).
Nice‑to‑Have:
Exposure to healthcare or multi‑location environments.
Experience with EMR systems or similar platforms.
Familiarity with predictive analytics and ML workflows.
