Senior Data Engineer

Cricut•South Jordan, UT

9d•Onsite

About The Position

Cricut is hiring a Senior Data Engineer to shape the data foundation behind Cricut's product analytics, personalization, and AI initiatives. This role will own and evolve the event data platform, from app instrumentation through streaming and batch ingestion to the warehouse. The engineer will also build the pipelines and feature infrastructure that feed recommendation systems and machine learning models. This role sits at the intersection of data engineering and ML, requiring close collaboration with product engineering, data science, ML engineering, analytics, and experimentation teams to ensure data is reliable, well-modeled, and ready for both decision-making and production models.

Requirements

8+ years of experience in data engineering, including building and owning production pipelines at scale
Strong SQL and Python skills; experience with PySpark or Spark is a plus
Deep hands-on experience with AWS data services (S3, Glue, Redshift, DynamoDB, Lambda, IAM)
Production experience with Apache Airflow, including DAG design, dependency management, templating, alerting, and backfills
Experience with streaming and event ingestion (Kafka, Kinesis, SQS, or similar) and clickstream or product analytics data
Strong data modeling skills (dimensional and event modeling) and a clear sense of how data design affects downstream metrics
A track record of building data quality and observability frameworks, not just pipelines
Experience supporting ML systems in production: 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 alike

Nice To Haves

Experience with recommendation systems or personalization data (interaction logs, embeddings, candidate generation, ranking features)
Familiarity with SageMaker, AWS Batch, or MLOps tooling (model registries, experiment tracking, 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 (Iceberg, Delta, 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 next-generation event architecture
Build event data quality and observability: schema validation, instrumentation testing, anomaly detection, freshness and completeness monitoring, and lineage across pipelines
Handle late-arriving and out-of-order data correctly, using lookback reprocessing and idempotent, incremental loads that keep business metrics accurate
Build and maintain the data foundations for personalization and recommendation systems, including the 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, 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 new AI-powered product experiences, including interaction, generation, and feedback events that close the loop on model improvement
Develop well-designed fact and dimension models that power 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