7 days ago
South Jordan, UT, USASenior
Responsibilities
- Design, build, and operate scalable batch and streaming pipelines on AWS using Airflow, Glue, Kafka, S3, and Redshift.
- Own product event platform evolution, including schema design, event taxonomy, versioning, and migrations.
- Develop data quality and observability capabilities covering validation, instrumentation testing, anomaly detection, freshness, completeness, and lineage.
- Implement reliable handling of late-arriving and out-of-order data through lookback reprocessing and idempotent incremental loads.
- Build data foundations, feature pipelines, and feature-store infrastructure for personalization and recommendation systems.
- Support ML workflows on AWS Batch and SageMaker, including training-data generation, offline evaluation datasets, and model-output delivery.
- Instrument and model interaction, generation, and feedback data for AI-powered product experiences.
- Develop fact and dimension models for BI, ad-hoc exploration, and experimentation platforms.
- Tune warehouse performance and cost through distribution, sort, workload-management, unload, and serving strategies.
- Set engineering standards for code review, testing, CI/CD, documentation, and on-call practices, while mentoring engineers.
Requirements
- 8+ years of experience in data engineering, including ownership of production pipelines at scale.
- Strong SQL and Python skills; PySpark or Spark experience is a plus.
- Deep hands-on experience with AWS data services including S3, Glue, Redshift, DynamoDB, Lambda, and IAM.
- Production experience with Apache Airflow, including DAG design, dependency management, templating, alerting, and backfills.
- Experience with streaming and event ingestion technologies such as Kafka, Kinesis, or SQS, plus clickstream or product analytics data.
- Strong dimensional and event data-modeling skills and understanding of downstream metric impacts.
- Experience building data quality and observability frameworks.
- Experience supporting production ML systems through feature engineering, training datasets, feature stores, or model-serving data flows.
- Ability to lead cross-functional technical work, write design documents, and translate ambiguous business needs into architecture.
- Clear communication skills with engineers, data scientists, and business partners.
- Preferred experience with recommendation systems, personalization data, SageMaker, AWS Batch, MLOps tooling, analytics instrumentation, LLM or generative AI applications, A/B testing platforms, lake table formats, or Redshift data sharing.
- A passion for making, crafting, or creative tools is preferred.
Benefits
- Medical, dental, and vision coverage.
- 401(k) match, generous paid time off, tuition reimbursement, and a yearly lifestyle stipend.
- Employee discounts and a collaborative, creative work environment.
- In-office work at least 4–5 days per week; this is not a fully remote role.
- Relocation assistance is available.
- Employment is contingent on successfully completing a criminal background check.
- Cricut participates in E-Verify.
Tech Stack
Categories
Data Engineering
About Cricut
Cricut builds smart cutting machines, heat presses, and crafting tools with companion software and a content library used by crafters, educators, and small sellers. It sells hardware, materials, and subscriptions (including Cricut Design Space and Cricut Access) direct-to-consumer and through major retailers. Founded in 1969 and headquartered in South Jordan, Utah, the company is publicly traded on Nasdaq (CRCT) after its 2021 IPO.