Principal Data Architect

Position Summary:

With team members and customers in 39 countries around the globe, Host Papa is currently one of the fastest-growing web hosting companies with a wide range of products available. At its core, we provide individuals and small and medium-sized businesses with access to valuable tools and services critical to their online success, including a Website Builder service for making website creation an ultra-easy task for anyone.

Tailored to meet every user’s unique needs, our award-winning customer support, email, and cloud-based solutions keep Host Papa at the cutting edge of the web hosting industry and innovation by putting our customers first.

As the Principal Data Architect, you will define and drive the technical vision behind Cloud Blue’s data and insights platform, shaping how data is structured, integrated, and leveraged to support financial growth, product adoption, and predictive capabilities. You will lead the design and evolution of data architecture, translating business and stakeholder requirements into scalable data models and AI‑enabled solutions within a microservices-based, cloud-native SaaS environment.

You will work cross-functionally with product, engineering, and finance teams to solve complex data and machine learning challenges, helping build a modern, AI-driven platform that integrates APIs, relational and non-relational data sources, and intelligent workflows.

• Architect and design machine learning systems capable of processing millions of real-time data points, leveraging feature stores, real-time inference pipelines, and scalable model serving frameworks to ensure high performance and low latency.
• Drive architectural decision-making for data and ML systems through RFC processes, ensuring solutions are scalable, statistically sound, and future-proof.
• Contribute hands-on to data and ML challenges, including building data architectures and high-performance feature engineering pipelines.
• Collaborate closely with Dev Ops teams to ensure ML infrastructure (Kubernetes, cloud platforms, GPU clusters) is optimized for training and inference workloads.
• Define and evolve scalable data architectures that support advanced analytics, predictive modeling, and business growth.
• Mentor and guide Senior Data Scientists and ML Engineers, fostering strong practices in statistical rigor, MLOps, and systems thinking.
• Support other tasks or projects as assigned to meet team and business needs.

• Degree in a STEM field such as Computer Science, Engineering, or Applied Mathematics, or equivalent practical experience.
• 5+ years of combined experience across Data Engineering, Data Architecture, and Data Science.
• Proven experience designing and deploying large-scale, distributed data systems handling high transaction volumes.
• Strong expertise in cloud environments such as AWS, GCP, or Azure, and modern data platforms such as Snowflake, Databricks, or Big Query.
• Solid understanding of data modeling principles, including relational, dimensional, and No
  
  SQL approaches.
• Experience building and orchestrating data pipelines using tools such as Airflow, dbt, Spark, or Kafka.
• Knowledge of data governance, security, and compliance best practices.
• Advanced proficiency in Python and SQL, with experience using data science libraries such as pandas, Num Py, and scikit‑learn.
• Proven track record of building, training, and deploying machine learning models to solve real-world business problems.
• Experience applying MLOps principles to move models from experimentation to production-ready systems.
• Experience developing billing and rating systems for AI-driven or consumption-based models would be considered an advantage.
• Hands‑on experience integrating AI services or building advanced AI solutions such as RAG pipelines or API-based AI workflows…