Sr. Data Engineer

About the role

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.

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).
• Persistently 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.

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.
• Passionate about mentoring and elevating team capabilities.
• Curious and adaptable, with a focus on continuous learning and innovation.
• 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.

Qualifications

• Experience 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.
• Passionate about mentoring and elevating team capabilities.
• Curious and adaptable, with a focus on continuous learning and innovation.
• 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.

Skills

• SQL (advanced query optimization, complex transformations)
• Python for pipeline development, orchestration, and automation
• Git-based workflows, CI/CD pipelines, and environment promotion strategies
• Infrastructure-as-code and deployment automation
• Machine learning pipelines
• LLMs and AI-assisted development tools (e.g., GitHub Copilot, ChatGPT)
• Feature stores or model serving patterns
• Data preparation for AI/ML use cases

Benefits

• Medical, dental, and vision coverage
• Life and AD&D insurance
• Short and long-term disability coverage
• Paid time off
• Employee assistance program
• Participation in a 401k program that includes company match
• Additional voluntary benefits

Pay

The salary range for this position is $115,000 - $125,000. Pay offered will vary based on job-related factors such as location, experience, training, skills, and abilities.

Schedule

This role has been aligned to the Collaborator persona. Participation in this flexible schedule plays a key role in building a connected and successful team. In-office requirements vary by our work personas: Resident (5 days), Collaborator (4 days), Connector (2-3 days), Explorer (fully remote).