Senior Data Engineer

What You'll Do

• Spend approximately 70% of your time coding, building pipelines, and implementing solutions.
• Dedicate 30% to architectural design, standards definition, and technical guidance.
• Work alongside engineers and provide mentorship through code reviews, pair programming, and technical design sessions.
• Provide architectural guidance and hands-on mentorship to engineers.

Data Architecture & Design

• Define and document reference architectures, design patterns, and standards for the enterprise data platform.
• Create technical design documentation, data flow diagrams, and architectural decision records (ADRs).
• Establish data modeling standards, naming conventions, and best practices across the platform.

Architecture Governance

• Establish and maintain data modeling standards, design patterns, and architectural guidelines.
• Review and approve technical designs to ensure alignment with architectural principles and enterprise standards.
• Collaborate with stakeholders to define data governance policies and ensure compliance with security requirements.

Technical Mentorship & Collaboration

• Provide architectural guidance and hands-on mentorship to engineers through code reviews, pair programming, and technical design sessions.
• Share expertise in data vault modeling, dbt development, and cloud data engineering best practices.
• Foster a culture of technical excellence and continuous learning within the team.

Data Vault Implementation

• Design and implement data vault 2.0 modeling patterns to build a scalable, audit-friendly enterprise data platform.

Modern Data Engineering

• Build and maintain automated data pipelines using dbt (Cloud/Core), Python, and Snowflake.
• Implement comprehensive data quality testing frameworks using dbt tests, custom Python validations, and automated monitoring.

Cloud Data Platform Development

• Architect and implement an enterprise data platform on Snowflake, including automated deployment pipelines, data quality frameworks, and monitoring solutions.
• Enable reliable, scalable, and automated data workflows by implementing DataOps best practices for continuous integration, testing, deployment, and monitoring.
• Establish and maintain Snowflake security governance through role-based access control (RBAC).

External Data Integration

• Integrate and operationalize data from external systems such as CRM, ERP, and third-party platforms via secure cloud data sharing, CDC, and APIs.

DataMart & Dimensional Modeling

• Design and build data marts using dimensional modeling techniques (Kimball methodology).

ETL/ELT Pipeline Development

• Design and implement robust data transformation models using dbt, SQL, and Python.

Data Quality & Testing

• Implement comprehensive data quality testing frameworks using dbt tests, custom Python validations, and automated monitoring.

Cloud Migration Support

• Play an integral role in planning, designing, and implementing data migration strategies from legacy on-premises SQL Server systems to our modern Snowflake cloud platform.