Software Engineer, Data Platform

Genius AI is building technology to help local businesses run themselves. Genius AI products do the admin work that keeps practitioners from the work they love and the people they serve. From GlossGenius to Reception, its products work 24/7 on busywork like booking, marketing, client management, finances, staff and inventory management, and more.

Genius AI technology has saved businesses 106M in admin hours, earned them $2.0B in additional revenue, and facilitated more than 624M client touch points.

Every AI product Genius AI ships runs on top of a data platform, and right now, that platform is being rebuilt from the ground up to support it. As a Software Engineer on the Data Platform team, you'll own the architecture and infrastructure that moves data from raw ingestion to model-ready, at the scale of billions of transactions and 100,000+ businesses generating signal every day. This is the foundational layer that determines what our AI can do and how fast it can get there. The work is technically hard, the stakes are real, and the person in this role will directly shape the long-term direction of GlossGenius's data ecosystem.

You must be commutable to our San Francisco office and will operate in a hybrid environment. We default to being in-office 3–4 days per week with required attendance on Tuesdays and Thursdays.

• →Architect and evolve a scalable, cost-efficient lakehouse foundation using Snowflake and Clickhouse. Owning it end-to-end from design through operations and cost management
• →Design and implement core data models and pipelines that power analytics, ML, and AI product experiences across the platform
• →Build the data infrastructure that makes AI possible: pipelines that move data from raw ingestion to model-ready, with the reliability and performance that production ML systems require
• →Implement modern data orchestration patterns including medallion architectures, batch and streaming workloads, and large-scale ETL at production scale
• →Define technical standards and best practices that improve data quality, governance, and lineage across teams, and drive adoption across engineers who don't report to you
• →Use AI tooling to accelerate your own development workflow: from pipeline design and debugging to query optimization, and set the bar for what AI-assisted data engineering looks like on this team
• →Mentor engineers and foster a culture of ownership, operational excellence, and continuous improvement

• 5+ years of data engineering experience, with a strong background in data architecture, data modeling, and distributed data systems at production scale
• You've used AI to design pipelines, accelerate debugging, and push the quality of your own output, not just as an autocomplete tool
• Deep expertise in modern lakehouse technologies including Snowflake and Clickhouse, with hands-on experience architecting for reliability and cost efficiency
• Advanced proficiency in SQL and Python or Scala, including performance optimization and large-scale ETL design
• Proven experience building data infrastructure that supports AI and ML workflows, you understand what it takes to get data from raw to model-ready and have done it in production
• Strong ability to lead technical initiatives, set standards, and influence decisions across teams without relying on positional authority
• Clear communicator who can translate complex technical trade-offs to both engineers and non-technical stakeholders