Senior Data Engineer

Cricut•South Jordan, UT

2d•Onsite

About The Position

Cricut is hiring a Senior Data Engineer to shape the data foundation for product analytics, personalization, and AI initiatives. The role owns and evolves the event data platform, covering app instrumentation, streaming and batch ingestion, and the warehouse. It also builds pipelines and feature infrastructure for recommendation systems and machine learning models. The position works across data engineering and ML in close partnership with product engineering, data science, ML engineering, analytics, and experimentation teams. The engineer ensures data is reliable, well-modeled, and ready for decision-making and production models. The role is in-office at least 4–5 days per week and is eligible for relocation assistance.

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, SQS, or similar, and with clickstream or product analytics data.
Strong data modeling skills, including dimensional and event modeling, and understanding of how data design affects downstream metrics.
A track record of building data quality and observability frameworks.
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.
Must successfully complete a criminal background check upon hire.

Nice To Haves

Experience with PySpark or Spark.
Experience with recommendation systems or personalization data, such as interaction logs, embeddings, candidate generation, and ranking features.
Familiarity with SageMaker, AWS Batch, or MLOps tooling such as model registries, experiment tracking, and 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 Redshift data sharing.
A passion for making, crafting, or creative tools.

Responsibilities

Design, build, and operate scalable batch and streaming pipelines on AWS using Airflow (MWAA), Glue, Kafka, S3, and Redshift.
Lead the 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 lineage across pipelines.
Handle late-arriving and out-of-order data using lookback reprocessing and idempotent, incremental loads to maintain accurate business metrics.
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, such as 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, write design documents, and translate ambiguous business needs into sound architecture.