Sr. Data Engineer

Crocs, Inc.•Westminster, CO

17d•$115,000 - $125,000•Hybrid

About The Position

The Senior Data Engineer at Crocs, Inc. will design, build, and scale our next-generation cloud data platform. This role will play a critical part in developing a modern Snowflake-based data ecosystem, leveraging dbt for transformation, managed Airflow (Astronomer) for orchestration, and emerging AI/ML tooling to unlock advanced analytics and intelligent data products. This role will partner closely with analytics, data science, and business stakeholders to deliver reliable, scalable, and well-governed data solutions that power enterprise decision-making.

Requirements

Bachelor’s degree in Computer Science, Engineering, or related field (Master’s preferred).
3+ years of experience in data engineering or analytics engineering roles.
Proven experience building and scaling enterprise data platforms in the cloud.
Snowflake: Advanced experience in data modeling, performance tuning, and cost optimization.
DBT (Data Build Tool): Strong experience building modular transformations, tests, and documentation.
Airflow (Managed/Astronomer preferred): Expertise in DAG design, scheduling, and orchestration patterns.
Strong proficiency in SQL (advanced query optimization, complex transformations).
Strong proficiency with Python for pipeline development, orchestration, and automation.
Experience with Azure (Data Lake, Key Vault, Functions) or similar cloud platforms.
Familiarity with modern data stack tools (e.g., Kafka/Event Hub, REST APIs, SFTP ingestion patterns).
Deep understanding of dimensional modeling (Kimball) and modern ELT practices.
Experience designing data models for BI tools (e.g., Power BI, Looker).
Experience with Git-based workflows, CI/CD pipelines, and environment promotion strategies.
Familiarity with infrastructure-as-code and deployment automation.
Experience working with or supporting machine learning pipelines.
Familiarity with: LLMs and AI-assisted development tools (e.g., GitHub Copilot, ChatGPT), Feature stores or model serving patterns, Data preparation for AI/ML use cases.
Strong communication skills with the ability to translate technical concepts for business stakeholders.
Highly collaborative with a proactive, ownership-driven mindset.
Passion for mentoring and elevating team capabilities.
Curious and adaptable, with a focus on continuous learning and innovation.

Nice To Haves

Experience migrating from legacy platforms (e.g., Databricks, Informatica) to modern stacks.
Familiarity with data cataloging and governance tools (Alation, Collibra).
Experience with real-time or streaming data pipelines.

Responsibilities

Design and implement scalable data pipelines using ELT patterns in Snowflake.
Build and maintain transformation workflows using DBT, ensuring modular, testable, and reusable models.
Develop and orchestrate pipelines using managed Airflow (Astronomer) with production-grade DAG design.
Support a medallion architecture (Bronze, Silver, Gold) with clear data contracts and domain ownership.
Define and implement scalable data models using dimensional modeling (Kimball) and modern analytics engineering practices.
Partner with domain teams to design business-aligned data models and curated data products.
Contribute to data architecture decisions around domain-oriented design and data sharing strategies.
Apply software engineering best practices including: CI/CD pipelines (GitHub Actions, Azure DevOps), Automated testing (dbt tests, data quality frameworks), Code reviews and version control.
Optimize performance and cost across Snowflake (warehouses, query tuning, storage optimization).
Design robust, scalable workflows in Airflow (Astronomer), including dependency management, retries, and observability.
Automate ingestion and transformation processes using event-driven or scheduled patterns.
Improve reliability and reduce manual intervention through proactive monitoring and alerting.
Enable data for AI/ML use cases, including feature engineering and model-ready datasets.
Integrate modern AI tooling such as: LLM-powered data workflows (e.g., metadata generation, query generation, documentation), Data quality anomaly detection, AI-assisted development (e.g., code generation, testing acceleration).
Collaborate with data science teams to operationalize ML models and pipelines.
Implement data quality checks, lineage tracking, and monitoring across pipelines.
Ensure compliance with data governance standards and secure data access patterns.
Contribute to cataloging and documentation (e.g., Collibra, Alation, or similar tools).
Partner with analytics, product, and business stakeholders to translate requirements into scalable solutions.
Mentor junior and offshore engineers and promote best practices across the team.
Drive continuous improvement and adoption of modern data engineering standards.