Scope of Job: The Principal Engineer, Data Platform both informs and implements the data architecture vision, delivering a modern cloud data platform that includes infrastructure, data pipelines, and analytics readiness. The role owns end-to-end data flow from source systems through Snowflake to consumption-ready data products, guiding engineers and managing business stakeholders for analytics, integration, and reporting. The role designs scalable, reliable data architectures that deliver measurable business value while creating and aligning with industry-wide design standards and quality criteria. The Principal Engineer Data Platform informs the company’s data architecture criteria through influence and leadership and translates the vision into actionable designs. This role actively contributes to and grows the company’s architecture community, providing feedback to improve the company’s architecture, processes, and governance. This role combines architecture, platform engineering and analytics engineering to deliver a complete data solution; proposing designs that reflect best architecture and engineering practices and industry standards. While final architectural decisions remain with the architecture function, this role acts as a peer and contributes richly to the quality of decisions made in the interest of architecture and engineered solutions. The Principal Engineer collaborates closely with several cross-functional interests: SAP Analytics Engineers, Integration Engineers, Analytics and Insights Engineers, Product Management, and Business Stakeholders to translate strategy into concrete data solutions and value.

This position is Hybrid based in San Diego, CA (Rancho Bernardo). Hybrid is three days a week in office, typically Tuesday, Wednesday, and Thursday. 

• Designs and Implements cloud-native data integrations using APIs and AWS services (S3, Glue, Lambda) to bring data into Snowflake from various sources. This role informs, then implements architecture patterns defined by the Data Architect and within standards and guidelines set by the Data Engineering Manager
• Designs, builds, and maintains data transformation models using dbt, creating dimensional models, data marts, and business logic based on specifications from SAP Reporting Analysts and business stakeholders
• Manages Snowflake platform infrastructure including environment setup, performance optimization, cost management, security configuration, and access controls
• Leads cross-functional collaboration and aligns engineering work with business goals by working with business stakeholders, product management, analytics, governance, architecture, and data engineering. Surfaces cross-team decisions to the inform the Principal Architect and Data Engineering Manager as appropriate.
• Applies established standards and criteria in the implementation of data quality frameworks, testing, monitoring, and alerting to ensure data accuracy and pipeline reliability across the platform.
• Contributes to and implements the vision for Data Architecture, translating business requirements into techpriorities.maps; presents progress, risks, and trade-offs to leadership and non-technical business audiences; and partners with Product Management to shape data platform capabilities aligned to business priorities
• Performs data integration tasks and partners with Reporting Engineers to optimize data models for visualization
• Documents data models, lineage, and technical architecture to support data governance initiatives
• Other duties as assigned

Supervision of Others: This role does not supervise any direct reports but has significant influence inside the business and Digital Technology. This role is seen as a highly skilled mentor.

Working Conditions: 95% of time is spent in the office environment utilizing computers (frequent use of various Microsoft software/programs), phones, and general office equipment. 5% of time is spent outside of the office visiting vendors’ and/or internal customers’ sites in addition to attending various conferences and meetings.

Fiscal Responsibilities: May contribute to the Data Engineering budget.

• Education: Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related technical field.

• Core Experience: 8+ years in data engineering or data platform roles, with a proven track record of delivering production-grade data platforms and leading technical teams.

• Cloud & Data Warehousing:

  • High proficiency with AWS cloud computing environments and associated data services.

  • Deep experience with cloud data warehouses (Snowflake strongly preferred).

• Engineering & Development:

  • Languages & Frameworks: Expert-level Python (scripting and data processing) and dbt (transformations, data modeling, and documentation).

  • Database Design: Mastery of SQL (complex queries, optimization, database design), dimensional modeling, star schemas, and data warehouse design patterns.

  • Data Architecture Practices: Advanced knowledge of change data capture (CDC), slowly changing dimensions (SCD), and incremental loading patterns.

• DevOps & Infrastructure:

  • Proficiency with Git version control and modern CI/CD practices.

  • Hands-on experience with Infrastructure as Code (IaC) tools (Terraform, CDK, CloudFormation, or Ansible).

• Experience with workflow orchestration tools (Airflow, Dagster, Prefect).

• Exposure to SAP S/4HANA, SAP HANA, or enterprise ERP environments.

• Experience operating within Agile/Scrum frameworks (sprint planning, retrospectives).

• Technical Leadership & Execution:

  • Translates data architecture visions, business requirements, and technical specifications into scalable, production-grade solutions.

  • Leads technical design discussions, defends proposals in architecture reviews, and aligns infrastructure needs (performance, cost, reliability) with business KPIs.

  • Conducts thorough code reviews to ensure maintainability, performance optimization, and quality, while mentoring junior engineers.

• Governance & Quality Assurance:

  • Embeds data quality testing and validation frameworks directly into system designs.

  • Implements data governance practices, including data lineage, metadata management, and classification standards.

  • Leverages data catalog platforms and Jira for transparent project tracking and metadata management.

• Problem Solving & Collaboration:

  • Drives root-cause analysis for technical incidents, communicating remediation plans clearly to both technical and non-technical stakeholders.

  • Demonstrates a versatile, self-starter mindset—effortlessly shifting between architecture, platform engineering, and analytics engineering in a growing, fast-paced environment.

  • Uses strong interpersonal and influence skills to build consensus across broad stakeholder groups while keeping leadership informed of progress, blockers, and trade-offs.

• Environment: 100% office-based environment utilizing standard office equipment (computers, phones, Microsoft Office suite).

• Physical Requirements: Ability to sit, stand, or walk for extended periods while operating computer equipment and reviewing digital documentation.