Senior Data Engineer

Cricut•South Jordan, UT

1d•Onsite

About The Position

Cricut is hiring a Senior Data Engineer to shape the data foundation behind its product analytics, personalization, and AI initiatives. The role owns and evolves the event data platform, from application instrumentation through streaming and batch ingestion to the warehouse, and builds the pipelines and feature infrastructure that support recommendation systems and machine learning models. It works closely with product engineering, data science, ML engineering, analytics, and experimentation teams to ensure data is reliable, well modeled, and ready for decision-making and production models. The engineer will design, build, and operate AWS data pipelines; lead the evolution of the product event platform; establish data quality and observability; and develop data foundations and feature pipelines for personalization and machine learning. The role also supports ML workflows, analytics and experimentation, warehouse performance, and engineering standards. This is an in-office role at least 4–5 days per week and is eligible for relocation assistance. Employment is contingent on successfully completing a criminal background check.

Requirements

At least 8 years of experience in data engineering, including building and owning production pipelines at scale.
Strong SQL and Python skills.
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, and with clickstream or product analytics data.
Strong data modeling skills in dimensional and event modeling, with an understanding of how data design affects downstream metrics.
Track record of building data quality and observability frameworks, beyond building pipelines alone.
Experience supporting production ML systems, including feature engineering, training datasets, feature stores, or model-serving data flows.
Ability to lead cross-functional technical work, write clear design documents, and turn ambiguous business needs into sound architecture.
Clear communication with engineers, data scientists, and business partners.
Ability to work in-office at least 4–5 days per week.
Successful completion of a criminal background check upon hire.

Nice To Haves

Experience with recommendation systems or personalization data, such as interaction logs, embeddings, candidate generation, or ranking features.
Familiarity with SageMaker, AWS Batch, or MLOps tooling such as model registries, experiment tracking, or pipeline orchestration.
Experience with analytics instrumentation tooling or tracking-plan governance.
Exposure to LLM or generative AI applications, such as vector stores, retrieval pipelines, or evaluation and feedback data.
Experience with A/B testing platforms and experiment metric pipelines.
Experience with data lake table formats such as Iceberg, Delta, or Hudi, or with Redshift data sharing.
A passion for making, crafting, or creative tools.
PySpark or Spark experience.

Responsibilities

Design, build, and operate scalable batch and streaming pipelines on AWS using Airflow (MWAA), Glue, Kafka, S3, and Redshift.
Lead evolution of the product event platform, including schema design, event taxonomy, versioning, and migrations to a next-generation event architecture.
Build event data quality and observability, including schema validation, instrumentation testing, anomaly detection, freshness and completeness monitoring, and pipeline lineage.
Handle late-arriving and out-of-order data using lookback reprocessing and idempotent, incremental loads to keep business metrics accurate.
Build and maintain data foundations for personalization and recommendation systems, including interaction, content, and project datasets used for model training and inference.
Partner with ML engineers to build feature pipelines and a batch plus low-latency feature store, for example from Redshift or S3 to DynamoDB, for real-time serving.
Support ML workflows on AWS Batch and SageMaker, including training data generation, offline evaluation datasets, and delivery of model outputs to downstream APIs.
Instrument and model data for AI-powered product experiences, including interaction, generation, and feedback events that support model improvement.
Develop fact and dimension models for BI, ad-hoc exploration, and experimentation platforms.
Tune warehouse performance and cost through distribution and sort strategies, workload management, and efficient unload and serving patterns.
Set engineering standards for code review, testing, CI/CD, documentation, and on-call practices, and mentor other engineers.
Collaborate with product engineering, data science, ML engineering, analytics, and experimentation teams; lead cross-functional technical work and translate ambiguous business needs into sound architecture.