Data Platform Engineer

Role Overview

• Design and own scalable data platforms for real-time and batch processing across Azure, AWS, and Snowflake
• Architect warehouse solutions, semantic layers, and data-sharing capabilities across domains
• Set the standards for data modeling, orchestration, lineage, observability, and quality
• Build the AI/ML data infrastructure: feature stores, training pipelines, vector databases, RAG architectures, and LLM-ready data products
• Evaluate and integrate AI-native tooling into the data platform
• Build and optimize robust ELT/ETL pipelines — low-latency, trusted, business-critical
• Lead technical decisions on orchestration, CI/CD, and infrastructure automation
• Leverage AI-assisted development practices (code generation, automated testing, AI code review) to raise velocity and standards
• Build self-healing, self-optimizing pipelines using ML-driven observability
• Own monitoring, alerting, and operational excellence for the data platform
• Ensure compliance with governance, security, masking, and regulatory standards
• Collaborate closely with data leadership today to co-create BetWarrior's data platform strategy, with the mindset to own and drive its long-term evolution
• Influence technical and operational decisions, acting as a strategic partner to the business as the data domain scales
• Mentor and pair with engineers and analysts, raising engineering standards across the team
• Drive experimentation, personalization, fraud prevention, and player analytics capabilities
• Champion data culture and democratize data access across the company.

Requirements

• 7+ years in Data Engineering, Data Platform, or Analytics Engineering
• Snowflake expertise: performance optimization, architecture, governance, advanced platform capabilities
• Solid experience with Azure and AWS for large-scale data processing
• Expert-level SQL and strong Python skills
• Deep understanding of modern data architecture: dimensional modeling, semantic layers, distributed systems
• Experience with DataOps, CI/CD pipelines, infrastructure automation, and orchestration frameworks
• Experience building data infrastructure for AI/ML workloads (feature stores, vector DBs, or LLM integration)
• Familiarity with AI-assisted engineering practices and tools
• Strong cross-functional stakeholder management and organizational communication skills
• Proven ability to mentor, elevate technical teams, and guide architectural vision
• Excellent English communication skills
• Fluency in Spanish is a valuable plus.

Tech Stack

• AWS
• Azure
• Distributed Systems
• ETL
• Python
• SQL

Benefits

• Health insurance
• Retirement plans
• Paid time off
• Flexible work arrangements
• Professional development