Senior Engineer, Platform Analytics

Job Description

Required Skills

Responsibilities

• Build and operate the data pipelines that feed Platform Analytics — event ingestion and entity/reference ingestion into Snowflake — to a high standard of reliability and quality.
• Build and operate the traditional data-engineering ELT to create a reliable source of truth in the data warehouse.
• Contribute to the data models, curating and modeling source-system data into trusted, conformed datasets and applying dimensional modeling and engineering best practices.
• Support production performance, reliability, and cost — performance tuning, monitoring and alerting, and resource management.
• Help evolve the platform toward its target-state architecture, implementing the design and providing architectural and scale recommendations.
• Lead the development lifecycle for your work — implementation, testing, and deployment — collaborating with the team to deliver and maintain the platform.
• Help mentor junior engineers and contribute to team standards for code quality and ways of working.
• Use AI coding tools productively in daily engineering work — accelerating development, testing, and debugging.
• Familiarity with high-performance OLAP serving layers (e.g., ClickHouse) for product- or customer-facing analytics.
• Experience reducing reliance on integration/middleware tooling by migrating transformation logic into governed models.
• Experience partnering with a data governance function on company-wide standards, schema/version control, and PII classification.
• Requires a minimum of 8 years of related or equivalent experience; or 6+ years with an advanced degree.
• Strong, current, hands-on experience designing and delivering data warehouses and analytical datasets — including data curation, integration, and data-quality processes.
• Strong SQL development and performance tuning on analytical databases (e.g., Snowflake).
• Experience with streaming and real-time data processing (e.g., Kafka) and with change-data-capture (CDC) ingestion for entity/reference data.
• 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.
• Productive, fluent use of AI coding tools in day-to-day engineering work.
• Ability to provide architectural, scale, and reliability recommendations, and to collaborate effectively within an Agile team.
• Good communication skills — a strong collaborator and trusted teammate, able to partner with the product and the team.