Principal Software Engineer, Data Platform

Role Overview

What You’ll Do

• Design and evolve semantic models for metrics, dimensions, entities, and business logic; establish standards for authoring, versioning, testing, and governance and guide technology strategy.
• Architect semantic layer consumption for Reporting and Agentic Analytics, partnering with adjacent teams to meet their product requirements.
• Own query performance, materialization, pre-aggregation, and cost optimization to support scale as data and demand grow.
• Build self-service metric onboarding, developer-friendly abstractions, documentation, validation, and governance guardrails so teams can extend the platform consistently.
• Lead design sessions, mentor engineers, align stakeholders, contribute to cross-platform architecture, and champion quality code and test coverage.
• Use AI coding tools daily, drive adoption, create team-specific contexts and workflows, and set standards for AI-assisted development.

What You’ll Bring

• 10+ years in software or data engineering, including large-scale, high-traffic, fault-tolerant systems.
• Deep semantic modeling, data engineering, data lakehouse, and data product experience, including platform abstractions consumed by multiple teams.
• Strong dbt experience; semantic layer technology such as dbt MetricFlow is highly preferred.
• Expert SQL and Python, including query optimization, materialization strategies, and performance tuning at scale.
• Experience with Snowflake, ClickHouse, or similar OLAP/columnar platforms; Spark and Kafka or Kinesis familiarity is also noted.
• Experience designing APIs and interfaces for domain-specific data products and leading system architecture and design for reliability and scale.
• Demonstrated daily use of AI coding tools such as Claude, Cursor, or Copilot.
• Strong communication and technical writing skills, user empathy, and a B.S., M.S., or PhD in Computer Science or a related field.

Highly Preferred Experience

• Semantic layers serving human analysts and programmatic or AI consumers; data governance, metric versioning, or data product catalogs.
• LLM-friendly data interfaces and metadata that help AI agents discover and query data; validation and quality frameworks such as Monte Carlo or Great Expectations.

Work Hours

• Flexibility to overlap with US working hours as needed to support international teams.

About ServiceTitan