Lead Data Architect

Summary

Join Hike and become a key player in building the Rush Gaming Universe's data ecosystem. You will define and evolve the architecture of Rush’s data ecosystem, aligning our platform and infrastructure with long-term goals. Lead architectural design and reviews, coach data engineers, and cultivate a performance-driven data culture. Own and optimize end-to-end data systems, ensuring trust, availability, and speed at scale. Design and build foundational data assets and reusable components for analytics, machine learning, and personalization. Champion governance, metadata, and access control frameworks. Architect self-serve data platforms and tooling, reducing engineering dependency and increasing velocity. Drive observability, reliability, and cost efficiency. Collaborate with cross-functional teams to drive unified data strategies and create business value through data. Be the primary evangelist for architectural clarity and long-term thinking in data.

Requirements

• 7+ years of experience building scalable data architectures, including modern data platforms, distributed systems, and enterprise-grade analytics stacks
• Proven track record of architecting end-to-end solutions on Azure and/or AWS, leveraging tools like Databricks, Microsoft Fabric, Delta Lake, and serverless compute
• Deep expertise in SQL, Python, and programming languages like .NET or Java, with experience designing APIs and data products in microservices environments
• Demonstrated experience building cloud-native, modular data platforms that support real-time, streaming, and batch workloads at scale
• Mastery of data modeling, data lakes, lakehouses, warehousing, mesh architecture, and building high-quality, reusable datasets for analytics and ML
• Hands-on experience integrating data infrastructure for AI/ML, supporting pipelines for personalization, predictive insights, and experimentation
• Comfort with managing complex architecture across large teams, enabling governed, high-performance, and cost-optimized data operations
• Strong understanding of security, observability, CI/CD for data, and data compliance frameworks such as GDPR and SOC2
• Excellent communication and leadership skills, able to align cross-functional stakeholders, mentor engineers, and influence product and business strategy
• Experience with data governance, lineage tracking, and metadata management, ensuring discoverability and reliability of all data assets

Responsibilities

• Define and evolve the architecture of Rush’s data ecosystem, aligning our platform and infrastructure with the long-term goals of gameplay intelligence, personalization, and monetization
• Set a bold, AI-forward data architecture roadmap, designing for extensibility, low-latency performance, and innovation in a high-growth gaming and fintech hybrid environment
• Lead architectural design and reviews, coach data engineers and analytics engineers on best practices, and cultivate a performance-driven, experimentation-friendly data culture
• Own and optimize end-to-end data systems — including ingestion, transformation, lineage, quality monitoring, and consumption layers — ensuring trust, availability, and speed at scale
• Design and build foundational data assets and reusable components — enabling analytics, machine learning, and event-driven personalization for our players
• Champion governance, metadata, and access control frameworks that scale, while empowering decentralized data ownership and discoverability across teams
• Architect self-serve data platforms and tooling for analytics, experimentation, and ML, reducing engineering dependency and increasing velocity for decision-making
• Drive observability, reliability, and cost efficiency in our platform with real-time monitoring, capacity planning, and automated alerting
• Work closely with Product, Engineering, Analytics, and Infra teams to drive unified data strategies, support experimentation, and create business value through data
• Be the primary evangelist for architectural clarity, technical excellence, and long-term thinking in data — influencing across domains and ensuring scalability as we grow

Preferred Qualifications

Track record of continuously evolving tech stacks and adopting emerging tools, including AI-assisted data engineering and LLM data prep techniques