BI Lead – Data Analytics Engineer - MTA

Location: Remote (US-based preferred)
Team: Data / Growth / Engineering
Reports to: Head of Marketing & Data

About Us

We’re building the future of healthcare intelligence. Our platform powers multi-touch attribution (MTA) and advanced analytics on top of a modern Python/FastAPI backend. To support our next phase of scale, we’re building a new data lake and BI stack from the ground up—designed for speed, compliance, and scalability.

We need a BI Lead / Data Analytics Engineer who is excited to architect, build, and operationalize this foundation while driving attribution insights and analytics excellence across the company.

The Role

This is a hands-on, build-from-scratch role. You’ll architect and implement the company’s first data lake, ingestion frameworks, and attribution pipelines, and scale them into a robust BI environment.

The ideal candidate is a seasoned data engineer with 7+ years of experience building enterprise-grade data platforms, but is eager to adopt and master our Python/async/FastAPI/Celery environment.

You’ll partner closely with marketing, product, and engineering to ensure our data infrastructure not only works but drives real business outcomes.

Responsibilities

Architect & Build a Data Lake
- Design and implement the company’s first centralized data lake to unify raw, structured, and semi-structured data.
- Define ingestion patterns for streaming and batch pipelines across internal apps, SaaS tools, and APIs (ad platforms, CRM, telehealth systems, etc.).
MTA & Attribution Models
- Build pipelines that support advanced multi-touch attribution models (rule-based, probabilistic, and ML-driven).
- Deliver accurate, timely, and explainable attribution outputs to support marketing spend decisions.
Data Engineering & Pipelines
- Own ETL/ELT frameworks using Python, SQL, and orchestration tools.
- Deploy workflows in a FastAPI + Celery async task environment.
BI & Analytics Enablement
- Define schemas and data models that feed dashboards, experimentation, and decision-making.
- Enable self-service analytics for marketing, product, and finance teams.
Governance & Standards
- Define data contracts, quality checks, documentation, and access control to keep the platform trustworthy and compliant.
Scale & Optimize
- Continuously improve cost efficiency, performance, and observability of the data stack.

Requirements

7+ years in data engineering, analytics, or BI roles.
Strong experience designing and implementing data lakes or warehouses from scratch.
Expertise with SQL and modern data warehouse/lakehouse technologies (Snowflake, BigQuery, Redshift, Delta Lake, etc.).
Strong Python engineering skills, or proven willingness to quickly ramp into Python async patterns, FastAPI, and Celery-based architectures.
Experience with ETL/ELT frameworks, pipeline orchestration, and API-based ingestion.
Familiarity with attribution, experimentation, or marketing analytics concepts.
Excellent communicator with ability to bridge technical and business teams.

Nice-to-Haves

Exposure to marketing/healthcare data ecosystems (GA4, Freshpaint, Braze, ad platforms, EHRs).
Hands-on experience with distributed computing frameworks (Spark, Dask, etc.).
Experience leading a small team or mentoring junior engineers.
Familiarity with HIPAA or other regulated environments.

Why Join Us

Lead the end-to-end build of a greenfield data lake and BI environment.
Tackle one of the hardest problems in healthcare tech: attribution and compliance at scale.
High-impact role where your architecture decisions will shape the company for years.

Grow alongside a world-class engineering and growth team, with direct exposure to cutting-edge backend and marketing systems.

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