Data Engineer

Cricut, Inc.
South Jordan, UT, United States
4 days ago
Apply on www.dice.com
Prepare application

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
8 years minimum
Working hours
Regular working hours
Job source

Tech stack

Training Data A/B Testing Application Programming Interfaces (APIs) Artificial Intelligence Airflow Amazon Web Services Amazon S3 Data Analysis Code Review Continuous Integration Data as a Services Data Architecture
+26 more
Information Engineering Data Sharing Software Design Documents Amazon DynamoDB Identity and Access Management Python (Programming Language) Machine Learning Recommender Systems Power BI Standard Sql Data Streaming Management of Software Versions Usage Analysis Feature Store Feature Engineering Large Language Models Apache Spark Data Lakes Pyspark Templating Apache Kafka Machine Learning Operations Functional Programming Amazon Simple Queue Service (SQS) Data Pipelines Amazon Redshift

Job description

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 Do

  • Design, build, and operate scalable batch and streaming pipelines on AWS using Airflow (MWAA), Glue, Kafka, S3, and Redshift
  • Lead the evolution of our 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

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, * 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

About the company

Cricut empowers people to make and personalize almost anything-from custom cards and apparel to everyday items and home dcor. Our smart cutting machines, design apps, and materials make creativity easy and accessible for everyone. We believe everyone is born creative, and our mission is to put the power of handmade into the hands of all. With a passionate community of Makers around the world, Cricut helps turn inspiration into real, tangible creations-one project at a time., At Cricut, we take care of our people. Enjoy competitive Medical, Dental, and Vision coverage, a 401(k) match, generous PTO, tuition reimbursement, and a yearly lifestyle stipend to support your wellness and passions. You’ll also receive exclusive employee discounts-and best of all, you’ll be surrounded by some of the most talented, creative, and curious minds out there.

A Quick Note Before You Apply…

Cricut is in an exciting chapter of transformation. We’re evolving fast-refining our strategy, growing our teams, and raising the bar across everything we do. This is an incredible opportunity for the right kind of person-but it’s not for everyone.

We’re looking for A-players-people who thrive in dynamic environments, turn challenges into momentum, and consistently deliver their best work. If that sounds like you, read on.

Here’s what makes someone a great fit for this role (and for this moment at Cricut):

  • You move with urgency. You don’t wait for perfect clarity to act-you start, learn, and adjust.
  • You set high standards. You take ownership, deliver quality, and hold yourself accountable.
  • You stay focused when things move fast. You prioritize what matters most and tune out the noise.
  • You collaborate like a pro. You elevate others, communicate clearly, and bring a low-ego, high-output energy.
  • You embrace AI as part of your toolkit. From idea exploration to data analysis and creative problem-solving, you leverage AI to accelerate innovation and amplify impact-because technology and creativity go hand-in-hand here.

One More Thing (It’s a Big One)

This role is in-office at least 4-5 days per week. We believe real collaboration, innovation, and culture are built face-to-face. If you’re energized by working alongside smart, kind, creative people-and love those hallway conversations that spark the next great idea-you’ll feel right at home.

If you’re looking for a fully remote role, this may not be the right fit. But if you’re excited by challenge, purpose, and building something better-let’s make something amazing together.

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on www.dice.com
Prepare application

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

3:03 min

Career evolution in data engineering and AI platforms

Maria Apazoglou · Coffee With Developers

4:32 min

Harnessing Spark with Python using PySpark and Py4J

Ayon Roy · LIVE

2:19 min

Introduction to Apache Airflow for advanced orchestration

Alan Mazankiewicz · LIVE

1:24 min

Moving the semantic layer upstream to avoid vendor lock-in

Piotr Menclewicz Piotr Menclewicz · Europe 2026 Virtual

3:37 min

Scaling machine learning pipelines from prototypes to petabytes

Julian Joseph · LIVE

3:09 min

Balancing data science skillings alongside systems engineering rigor

Nico Schmidt · LIVE

Videos

See all

Related articles

See all