Senior Data Engineer

We're hiring a Senior Data Engineer to shape the data foundation behind Cricut's product analytics, personalization, and AI initiatives. You'll own and evolve our event data platform, from app instrumentation through streaming and batch ingestion to the warehouse. You'll also build the pipelines and feature infrastructure that feed our recommendation systems and machine learning models.This role sits where data engineering meets ML. You'll work closely with product engineering, data science, ML engineering, analytics, and experimentation teams. Together you'll make sure our data is reliable, well-modeled, and ready for both decision-making and production models.What You'll DoDesign, build, and operate scalable batch and streaming pipelines on AWS using Airflow (MWAA), Glue, Kafka, S3, and RedshiftLead the evolution of our product event platform, including schema design, event taxonomy, versioning, and migrations to next-generation event architectureBuild event data quality and observability: schema validation, instrumentation testing, anomaly detection, freshness and completeness monitoring, and lineage across pipelinesHandle late-arriving and out-of-order data correctly, using lookback reprocessing and idempotent, incremental loads that keep business metrics accurateBuild and maintain the data foundations for personalization and recommendation systems, including the interaction, content, and project datasets used for model training and inferencePartner with ML engineers to build feature pipelines and a batch plus low-latency feature store (for example, Redshift or S3 to DynamoDB) for real-time servingSupport ML workflows on AWS Batch and SageMaker, including training data generation, offline evaluation datasets, and delivery of model outputs to downstream APIsInstrument and model data for new AI-powered product experiences, including interaction, generation, and feedback events that close the loop on model improvementDevelop well-designed fact and dimension models that power BI, ad-hoc exploration, and experimentation platformsTune warehouse performance and cost through distribution and sort strategies, workload management, and efficient unload and serving patternsSet engineering standards for code review, testing, CI/CD, documentation, and on-call practices, and mentor other engineers