Data Engineer

Company Description

Cricut® makes smart cutting machines that work with an easy-to-use app, an ever-growing collection of materials, and crafting essentials to help you design and personalize almost anything — custom cards, unique apparel, everyday items, and so much more.

Let’s make.

Overview

We believe everyone is born creative. We’re a diverse tapestry of thinkers, dreamers, givers, DIYers, handi-workers, artisans, and forever and always architects of things.

At Cricut, we place the power of handmade into the hands of all. We give you beautiful, easy-to-master tools so you can make something unique, remarkable, perfect. We surround you with ideas, community, inspiration, and encouragement to take your creativity further than you ever imagined. And as a community, we celebrate the exhilarating act of making every single day.

So, make that handcrafted card that feels like a hug. Design a shirt for fun, for family, or for a full-blown business. Craft with a passion or for a purpose. Make something big and bold, itsy-bitsy, amazingly ambitious, or just plain silly. Whatever you make, just make your heart out. Because here’s the remarkable truth: When we all make together, we make all things possible.

Let’s make.

Job Description

Job Description

This data engineering role requires expertise in AWS infrastructure, focusing on maintaining data pipelines and systems. The ideal candidate should be proficient in big data technologies like Spark, Kafka/Kinesis, and DynamoDB, as well as working with REST APIs. They should have experience with ETL processes, workflow management tools such as AWS Glue and Airflow, and data warehousing solutions like RedShift or Databricks (any one of these or any other data warehouse will work). The role involves close collaboration with the ML team, necessitating knowledge of feature engineering, Feature Store, and the ability to manage ML pipelines for training, evaluation, and continuous performance monitoring. Strong skills in deploying services, streaming, and ETL jobs are essential. The data engineer should also possess business acumen to understand and analyze metrics and be capable of setting up pipelines to address specific business use cases.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines and ETL processes to support ML models training and analysis purposes.
• Collaborate with data scientists and ML engineers to understand data requirements and build the necessary infrastructure.
• Optimize and manage data storage solutions in AWS, ensuring efficient data retrieval and processing.
• Implement best practices for data governance, privacy, security, and compliance.
• Develop and maintain data documentation, schema designs.
• Monitor and troubleshoot data pipelines, ensuring high availability and performance.
• Perform data cleaning, transformation, and integration from various data sources.
• Develop and deploy REST APIs to support data integration and retrieval.
• Utilize Airflow to schedule and monitor model training pipelines and data generation pipelines.
• Ensure continuous evaluation and improvement of model performance by setting up robust pipelines.
• Ensure data quality and integrity through rigorous testing and validation processes.
• Stay updated with the latest industry trends and technologies in data engineering and ML.

Qualifications

• Bachelor's degree in computer science, Engineering, or a related field. A master's degree is a plus, though not necessary.
• 5+ years of experience in data engineering or a related role.
• Strong experience with AWS services, including S3, Redshift, Glue, and Lambda, would be useful in performing the role.
• Proficiency in SQL and experience with relational databases. An understanding of how to use these database systems in detail would be expected.
• Strong programming skills in Python or Java.
• Experience with big data technologies such as Hadoop, Spark, or Kafka or similar systems.
• Familiarity with data modelling, ETL processes, and data warehousing concepts.
• Excellent problem-solving skills and attention to detail.
• Strong communication skills and the ability to work collaboratively in a team environment.
• Experience with CI/CD pipelines and version control systems like Git is compulsory.

Preferred Qualifications

• Experience with ML tools and frameworks, such as TensorFlow, PyTorch, or scikit-learn is not a prerequisite. However, understanding of ML models, features, feature engineering, ML inference from the perspective of data systems would be necessary to perform this role.
• Knowledge of containerization technologies like Docker and orchestration tools like Kubernetes.
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