Principal Engineer, Platform Analytics

Job Description

Required Skills

Responsibilities

• Own the architecture of Platform Analytics and the key technical decisions while preserving the consistency guarantee and meeting its latency and freshness targets.
• Design, build, and operate the data pipelines and serving layers hands-on: event ingestion and entity/reference ingestion into Snowflake, dbt-governed metric definitions, and a high-performance OLAP serving layer behind a single Analytics API.
• Own production performance, reliability, and cost — performance tuning, monitoring and alerting, and resource management — for a customer-facing system.
• Curate and model source-system data into trusted, conformed datasets, applying dimensional modeling and engineering best practices.
• Build and operate the traditional data-engineering ELT the product depends on to the same standard as the platform-analytics path.
• Set the technical bar for the team — code quality, design standards, and ways of working — and mentor senior and mid-level engineers, raising the team's ability to make and hold sound technical decisions.
• Partner with the Product Manager and stakeholders to sequence the architecture work ahead of the dependent product roadmap, and communicate technical direction and trade-offs to both technical and non-technical audiences.
• Use AI coding tools productively in daily engineering work — accelerating development, testing, and debugging — and help establish effective patterns for their use across the team.
• Experience building customer-facing analytics products where metric consistency across UI and API is a hard requirement.
• Experience taking a system from a launch/MVP design through to a scaled production architecture.
• Familiarity with low-latency lookup stores (e.g., Redis) for instant-response use cases.
• Requires a minimum of 12 years of related or equivalent experience; or 8+ years and an advanced degree.
• Deep, current, hands-on expertise designing and delivering data warehouses and analytical data platforms — including data curation, integration, metadata management, and data-quality processes — with the ability to do this work personally, not only direct it.
• Expert SQL development and performance tuning on analytical databases (e.g., Snowflake), with the judgment to hold low-latency, high-concurrency query performance at scale.
• Experience with streaming and real-time data processing (e.g., Kafka) and with change-data-capture (CDC) ingestion for entity/reference data.
• Experience designing and operating a high-performance OLAP serving layer for customer- or product-facing analytics (e.g., ClickHouse), or strong transferable equivalent.
• Experience with dimensional data modeling and with source control, testing, and deployment workflows for ELT (e.g., dbt, git-based CI/CD).