Software Engineer, Data Platform

GlossGenius is the AI-powered system behind the world’s most meaningful appointments, helping 100,000+ service businesses earn more revenue and free up time for the work they love. Our agentic workforce gets more clients in the door, grows profit per appointment, and keeps clients coming back — doing the jobs owners never had time for and couldn’t justify hiring to fill. Businesses on GlossGenius process billions in annual payment volume, and see 65% more revenue using GlossGenius Payments by growing ticket size, rebooking clients at checkout, and saving on processing fees.

This role is at the center of building a cohesive, reliable, and AI-ready data platform powering all aspects of GlossGenius’ data platform and infrastructure. Core responsibilities include building out the operational data ingestion, event streaming, and reliability practices that our analytics engineers, ML engineers, and product teams depend on. If you thrive on both shaping and building end-to-end data pipelines serving customers in many roles, this will feel right.

You must be commutable to our SF office. We default to being in-office 3 days per week.

• →Architect and build the core ingestion pipelines that move data from production systems, relational databases, and distributed datastores into our Snowflake and Clickhouse warehouses via near-real-time streaming and batch workflows
• →Own data platform reliability end-to-end: monitoring, incident response, SLOs, and the unglamorous work of keeping things running for 100,000+ service businesses
• →Design and implement core data models and medallion architectures that serve analytics, ML, and product experiences
• →Drive CI/CD for data infrastructure; pipeline deployments, schema changes, cluster configuration
• →Define technical standards for data quality, governance, cost attribution, and performance across the platform
• →Shape the long-term roadmap for GlossGenius's data ecosystem, including build-vs-buy decisions, vendor evaluation, and the evolution toward a fully governed lakehouse
• →Mentor engineers and raise the team's bar on operational excellence, distributed systems thinking, and ownership

• 5+ years of experience in data engineering or software engineering with a strong data infrastructure focus, ideally at a company running real distributed systems in production, not just warehousing SaaS
• Production experience with distributed databases and messaging systems: you've worked with something like AWS Aurora SQL, CockroachDB, Cassandra, Kafka, or comparable technologies at scale
• Deep expertise in real-time and streaming data patterns: CDC, Kafka, Spark Structured Streaming, or equivalents
• Strong command of Snowflake and ClickHouse (or comparable modern data warehouse technologies), and compute / storage optimization
• Advanced proficiency in Python or Scala and SQL
• Experience with large-scale ETL or ELT design and performance tuning
• Desire to work in a fast-paced, scrappy environment
• Ability to demonstrate operational excellence through monitoring, alerting, cost management, and continuous delivery practices
• Familiarity with AI/ML data workflows, building pipelines that feed or serve production ML models
• Integrated AI tooling into your development workflow in a meaningful way
• Excellent communication skills with the ability to translate technical trade-offs to both engineers and non-technical stakeholders