Principal Data Engineer

Base pay range: $/hr - $/hr

Who We Are: Xponential Fitness, Inc. (NYSE: XPOF) is a leading global franchisor of health and wellness brands. Through its mission to make boutique health and wellness experiences accessible to everyone, the company operates a diversified platform of five brands spanning across verticals including Pilates, barre, stretching, strength‑training, and yoga. In partnership with its franchisees, Xponential brands offer energetic, accessible, and personalized workout experiences led by highly qualified instructors in studio locations across 49 U.S. states, Puerto Rico, and 30 countries globally.

Xponential’s portfolio of brands includes Club Pilates, the largest Pilates brand in the United States;
Stretch Lab, a concept offering one‑on‑one and group stretching services;
Yoga Six, one of the largest franchised yoga brands in the United States;
Pure Barre, a total body workout that uses the ballet barre to perform small isometric movements and is one of the largest Barre brands in the United States; and BFT, a functional training and strength‑based program.

The Principal Data Engineer will lead the design and evolution of Xponential Fitness’s enterprise data architecture, enabling scalable, secure, and high‑performing data infrastructure that powers real‑time analytics, AI/ML capabilities, and strategic decision‑making across the organization. This role sits at the intersection of data strategy and engineering execution — responsible for building an integrated data ecosystem that supports Xponential’s diverse portfolio of brands and digital platforms.

The principal data engineer will partner cross‑functionally with leaders in AI, application development, business intelligence, and cybersecurity to ensure the right data is available, trustworthy, and actionable across the enterprise. This leader will play a critical role in advancing the company’s data modernization agenda, establishing best‑in‑class data practices, and unlocking value through insights, automation, and innovation.

• Design and implement resilient, cloud‑native data architectures supporting both batch and real‑time pipelines.
• Lead the ingestion, transformation, and orchestration of data via Fivetran, Apache Airflow, and Python‑based ETL/ELT.
• Standardize pipelines from member management and point of sales systems, digital platforms, and Mar Tech tools into a centralized lakehouse and warehouse.
• Partner with software engineering teams to ensure pipelines are CI/CD‑enabled using Git Hub Actions and Code Pipeline.

• Optimize compute, storage, and processing layers to ensure scalable, secure, and cost‑effective data operations.
• Integrate modern container orchestration, caching, and task automation approaches to support data enrichment, transformation, and delivery at scale.
• Leverage infrastructure‑as‑code and CI/CD pipelines to standardize deployments and reduce operational overhead.
• Align data platform architecture with application and Dev Ops workflows to support consistent, governed, and observable services across brands and environments.

• Collaborate with AI engineers to enable end‑to‑end MLOps, feature engineering pipelines, and training data provisioning.
• Ensure pipelines support model retraining, scoring, and inference workloads across ECS and Lambda environments.
• Prepare time‑series, transactional, and behavioral datasets for model consumption.

• Define and enforce data governance policies including lineage, metadata management, and data quality rules.
• Implement encryption, RBAC, and masking strategies to protect personal and sensitive business data.
• Ensure infrastructure and data flows meet regulatory and contractual obligations (e.g., SOX, PCI, GDPR).

• Instrument data workflows with Cloud Watch, Kinesis Firehose, Sumo Logic, Sentry, and New Relic for real‑time visibility.
• Tune Snowflake performance, control costs, and monitor data freshness across the platform.
• Automate validation and anomaly detection to ensure…