Data Engineer

Team Overview

Your Impact

Data Architecture and Pipelines

• Lead exploratory data analysis across structured and unstructured datasets to inform architectural decisions.
• Translate complex business requirements into scalable data solutions integrating non-ERP sources with the enterprise data warehouse.
• Architect and oversee scalable ETL and ELT pipelines that publish data to Snowflake.
• Define and oversee analytics-ready dbt data models with modular design, testing, and CI/CD integration.
• Lead a reusable semantic layer supporting AI, agentic solutions, dashboards, and enterprise applications.

AI and Data Consumption

• Integrate Snowflake Cortex and other AI or LLM capabilities into data pipelines.
• Lead advanced dashboards, reports, conversational analytics, and agentic solutions.
• Apply databases, cloud services, and scripting languages to data engineering work.

Quality, Governance, and Collaboration

• Contribute to data cleansing, integrations, data quality frameworks, compliance, observability, lineage, governance, and access control.
• Serve as a technical liaison with data architects, analysts, business stakeholders, and engineering teams.
• Drive technical governance, CI/CD, and version control standards across the modern data stack.
• Provide technical guidance and mentorship to junior data engineers.

Minimum Qualifications

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related technical field, or equivalent practical experience.
• 8+ years of progressive data engineering experience with cloud data warehousing and modern data platforms.
• 4+ years leading or architecting AI and agentic solutions on Snowflake, AWS, or GCP.
• Expert-level Snowflake Data Cloud proficiency, including advanced features, security, and operational practices.
• Expert-level dbt proficiency for complex data models, testing, and CI/CD integration.
• Mastery of SQL and Python for scalable processing, automation, and API integration.
• 7+ years with BI or reporting tools such as Sigma or Power BI.

Technical Experience

• Experience with cloud-native data processing and orchestration services such as AWS Glue, Lambda, Step Functions, GCP Dataflow, or Cloud Composer.
• Demonstrated experience applying Agile, Scrum, or Kanban methodologies in a data engineering environment.
• Ability to deliver outcomes with minimal supervision and provide technical guidance to junior data engineers.

Preferred Qualifications

• Expertise implementing data quality, observability, and lineage frameworks across enterprise data pipelines.
• Advanced Snowflake capabilities such as zero-copy cloning, secure data sharing, external tables, Snowpark, dynamic tables, or MCP gateways.
• Experience defining enterprise data and solution architectures, including data mesh or data fabric principles.
• Experience with broader analytics ecosystems such as Tableau or ThoughtSpot and complex downstream consumption patterns.
• Experience guiding technical teams and presenting complex technical concepts to senior leadership.
• Snowflake Gen AI certification is preferred.