Job Summary

We are seeking a Snowflake Data Engineer with strong expertise in Snowflake, DBT, and Airflow to design, build, maintain, and optimize enterprise data warehouse and data engineering solutions. The role requires deep hands-on experience with Data Vault 2.0, dimensional modeling, modern ELT pipelines, data quality, performance optimization, and enterprise data architecture. The ideal candidate will be capable of owning the full data modeling lifecycle, partnering with Agile teams and stakeholders, troubleshooting complex data and performance issues, and mentoring technical resources on data warehousing and performance best practices.

Key Responsibilities
• Design, build, and maintain enterprise data warehouse solutions using modern data warehousing methodologies, with a strong focus on Data Vault 2.0.
• Implement Data Vault 2.0 models, including Hubs, Links, Satellites, Raw Vault, Business Vault, and downstream Information Marts.
• Design and implement dimensional data models using Star and Snowflake schemas for analytics, reporting, and downstream consumption.
• Own the end-to-end data modeling lifecycle from source analysis through conceptual, logical, and physical models.
• Define appropriate data grain, conformed dimensions, and consistent business definitions across the data warehouse.
• Partner with business and technical stakeholders to translate requirements into scalable and maintainable data models.
• Architect, develop, and optimize ELT pipelines using Snowflake, Airflow, DBT, and Python.
• Implement metadata-driven and reusable pipeline patterns to improve scalability and accelerate onboarding of new data sources.
• Design and implement data quality checks, reconciliations, and validation frameworks to ensure data accuracy, completeness, and consistency.
• Ensure pipelines support incremental loads, historization, and change data capture (CDC) patterns where applicable.
• Monitor and optimize data warehouse performance, query execution, data volumes, pipeline runtimes, and analytical workloads.
• Identify and resolve performance bottlenecks across ELT processes, transformations, pipelines, and data warehouse workloads.
• Analyze data issues, defects, and test results and perform root cause analysis across pipelines, models, and transformations.
• Support data archiving, purging, and lifecycle management strategies to maintain optimal data warehouse performance.
• Work proactively with Agile teams to identify and resolve defects and deliver production-ready data solutions from design through deployment.
• Validate performance, stability, scalability, and reliability for Scrum initiatives.
• Develop predictive and prescriptive solutions in the big data arena using application performance monitoring and data engineering capabilities.
• Communicate critical issues and status updates to Agile teams in a timely manner.
• Mentor engineers and technical resources on data warehousing, Data Vault modeling, performance optimization, and best practices.

Required Qualifications
• BA/BS in a related technical field or equivalent combination of education and work experience.
• 10+ years of experience in Information Technology.
• 10+ years of hands-on experience in Data Warehousing, Data Engineering, and Data Architecture, with proven delivery of production-grade enterprise data platforms.
• Strong hands-on experience with Snowflake, DBT, and Airflow.
• Strong hands-on experience implementing Data Vault 2.0 in enterprise data warehouses, including Hubs, Links, and Satellites.
• Strong knowledge of Raw Vault, Business Vault, and Information/Data Mart design.
• Solid knowledge of dimensional modeling, including Kimball methodology, Star and Snowflake schemas, fact and dimension table design, slowly changing dimensions, and hybrid data warehouse architectures.
• Experience designing scalable, auditable, and historized data models for enterprise reporting and analytics.
• Experience building and managing data pipelines using Snowflake, Airflow, DBT, and Python following modern ELT patterns.
• Expertise in performance tuning and optimization of data warehouse workloads, pipelines, and transformations.
• Proficiency in SQL and PL/SQL package development.
• Experience with source control solutions such as Git/GitLab and the Git flow process.
• Experience working with Bitbucket, Azure DevOps, GitLab, Jira, and Confluence.
• Ability to analyze complex data and performance issues, identify bottlenecks, and perform root cause analysis.
• Ability to work effectively in Agile/Scrum environments and deliver production-ready solutions.
• Strong analytical, problem-solving, communication, and collaboration skills.

Preferred Qualifications
• Experience with Azure Cloud architecture, deployment, and optimization.
• Experience developing strategies for data archiving, purging, and data lifecycle management.
• Experience identifying performance bottlenecks and supporting development teams with rapid root cause analysis.
• Strong understanding of Application Performance Monitoring and its application to big data environments.
• Experience developing predictive and prescriptive solutions in the big data arena.
• Experience mentoring technical resources on data warehouse and performance best practices.

Top 3 Skills
• Snowflake
• DBT
• Airflow