Principal Engineer, Platform Analytics

Pluralsight

1d•$167,200 - $220,000•Hybrid

About The Position

The Principal Engineer, Platform Analytics is the senior technical owner of Platform Analytics — the customer-facing analytics experience to deliver a single, trustworthy source of truth for the metrics customers use to measure the learning progress. As a senior individual contributor on the team, they own the architecture and the critical technical decisions. This is a deeply hands-on role — they design, build, and operate the pipelines and serving layers themselves — spanning both the target-state platform-analytics architecture and the traditional data-engineering ELT that feeds it. They set the technical bar for the team, mentor junior engineers, and use AI coding tools fluently to build faster and better.

Requirements

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).
Experience with workflow orchestration tools (e.g., Apache Airflow) for scheduling, dependency management, and monitoring of production data pipelines.
Demonstrated ability to own architecture and make and hold the key technical decisions on a complex, business-critical system — exercising independent judgment on performance, reliability, scalability, and consistency.
Productive, fluent use of AI coding tools in day-to-day engineering work.
Excellent communication skills — able to align engineers, partner with product and non-technical stakeholders, and mentor more junior engineers.

Nice To Haves

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.

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.