Senior Data Engineer, AI & Analytics (Data Platform Lead)

Role Overview

Senior Data Engineer, AI & Analytics (Data Platform Lead)

MobexHealth is seeking a
Senior Data Engineer with strong AI, analytics, and automation experience
to architect and lead our end-to-end data ecosystem — transforming activity across our apps, tablets, kiosks, and integrations into
actionable insights, automated reporting, and AI-ready intelligence
.

This is not just a data engineering role — it is a
hybrid leadership position spanning data engineering, analytics, and AI enablement
, responsible for building the
foundation of how MobexHealth measures impact, drives decisions, and scales predictive capabilities
.

You will design the systems that answer critical business and clinical questions:

• How are members engaging across deployments?
• What interventions reduce ER utilization and hospitalizations?
• Where are care gaps closing — and where are they not?
• Which populations are at highest risk?
• How do outcomes vary across states, plans, and programs?

You will turn fragmented platform data into
structured intelligence, automated insights, and predictive models
that power product strategy, client reporting, and executive decision-making.

What You’ll Do

1. Senior Data Engineering – Build & Own the Data Platform

- Architect and implement a
scalable cloud-based data lake / warehouse
- Design ingestion pipelines for:
- Mobile applications
- Tablet and kiosk deployments
- Usage logs and telemetry
- API integrations
- External datasets (claims, eligibility, SDOH, etc.)
- Standardize and normalize
event-based data models across all products
- Build robust
ETL/ELT pipelines
for batch and real-time processing
- Ensure high availability, scalability, and performance of the data platform

2. Data Analytics & Insight Generation

- Translate raw engagement data into
clear, measurable KPIs and outcomes
- Define and standardize
event tracking frameworks across engineering teams
- Build curated datasets and analytical models to support:
- Engagement analytics
- ER diversion and utilization reduction
- Care gap closure
- Behavioral health follow-up (FUH/HEDIS)
- Multi-state and MCO performance comparisons
- Partner with Product and Leadership to define
what success looks like in data
- Act as a
bridge between technical data systems and business insights

3. Reporting, Dashboards & Automation

- Design and implement
automated reporting pipelines
- Build
executive dashboards
(internal + client-facing):
- KPI tracking
- Cohort analysis
- State and plan comparisons
- Deployment performance
- Eliminate manual reporting by creating:
- Automated data refresh pipelines
- Scheduled reports and alerts
- Self-service analytics capabilities
- Enable
real-time or near real-time insights
where needed
- Drive a culture of
data accessibility and transparency across teams

4. AI & Predictive Data Enablement

- Structure and optimize datasets for:
- Machine learning models
- Feature engineering pipelines
- Predictive analytics
- Build reusable
feature stores and data pipelines for AI initiatives
- Support development of:
- Risk stratification models
- Engagement prediction models
- Early intervention triggers
- Partner with AI/ML teams to ensure:
- Clean, labeled, and reliable training data
- Scalable model deployment pipelines
- Help transition MobexHealth from
descriptive analytics → predictive intelligence

5. Data Governance, Quality & Automation

- Establish enterprise-wide:
- Data standards
- Naming conventions
- Documentation and lineage
- Implement:
- Data validation frameworks
- Monitoring and alerting systems
- Automated pipeline testing
- Ensure
data integrity, security, and compliance
(HIPAA-aware design)
- Build scalable processes that support
multi-state, multi-client deployments

Required Qualifications

- 10+ years
of experience in Data Engineering or Data Platform Architecture
- Strong proficiency in
Python and SQL
- Experience building
scalable ETL/ELT pipelines
(batch + streaming)
- Experience with
cloud platforms (AWS, Azure, or GCP)
- Deep understanding of:
- Event-based data modeling
- Dimensional modeling
- Data warehousing best practices
- Proven experience working cross-functionally with
product, engineering, and business teams
- Strong analytical mindset with ability to translate data into
business value and outcomes

Preferred Qualifications (AI, Analytics & Automation Focus)

- Experience building or supporting
ML/AI data pipelines
- Familiarity with:
- Feature engineering
- Model training datasets
- Predictive analytics workflows
- Experience with
product analytics / telemetry data (mobile, web, IoT, etc.)
- Experience building
automated dashboards and BI solutions
(e.g., Tableau, Power BI, Looker)
- Knowledge of
healthcare, Medicaid, or regulated data environments
- Experience with
streaming technologies
(Kafka, Kinesis, Pub/Sub)
- Experience in a
startup or scaling environment
with evolving data needs