Scope of Job: 

Based in San Diego, CA, the Technical Lead implements the architecture vision provided by the Data Architect, 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 a small team of engineers and coordinating with analytics, integration, and reporting stakeholders. The role emphasizes designing scalable, reliable data architectures that deliver measurable business value while aligning with established design standards and quality criteria. The Technical Lead translates architecture criteria into actionable designs and implementations within approved guidelines, and actively contributes feedback to improve architecture, processes, and governance. This role combines platform engineering with analytics engineering to deliver a complete data solution and propose designs that reflect best practices and industry standards, with final architectural decisions remaining with the architecture function. Collaborates closely with SAP Analytics Engineers, Integration Engineers, Analytics and Insights Engineers, Product Management, and Business Stakeholders to translate strategy into concrete data solutions and value.

Work Location and Work Mode:  Hybrid with 3 days on-site in San Diego/Rancho Bernardo, CA.

• Implements cloud-native data integrations using APIs and AWS services (S3, Glue, Lambda) to bring data into Snowflake from various sources, following architecture patterns defined by the Data Architect and within standards and guidelines set by the Data Engineering Manager
• Builds and maintains data transformation models using dbt, creating dimensional models, data marts, and business logic based on specifications from SAP Reporting Analyst and bus Save iness 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 Data Engineering Manager as appropriate.
• Implements data quality frameworks, testing, monitoring, and alerting to ensure data accuracy and pipeline reliability across the platform, following established standards and criteria.
• Translates Data Architect’s vision and business requirements into technical roadmaps; presents progress, risks, and trade-offs to leadership and non-technical audiences; 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.

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.

• Degree: 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, including a history of leading technical teams and delivering production-grade data platforms.

• Cloud & Warehousing: Hands-on experience with cloud computing (AWS preferred) and cloud data warehouses (Snowflake highly preferred).

• Programming: Solid experience using Python for data engineering, scripting, and data processing.

• Development Tools: Proficient with Git version control and Infrastructure as Code (IaC) tools like Terraform, CDK, CloudFormation, or Ansible.

• Nice-to-Haves: Experience with workflow orchestration (Airflow, Dagster, Prefect) and exposure to ERP systems (SAP S4/HANA or SAP HANA).

• Data Transformation: Proficient with dbt for building transformations, models, and analytics documentation.

• SQL & Modeling: Mastery of SQL (complex queries, optimization, database design) and a deep understanding of dimensional modeling, star schemas, and data warehouse design patterns.

• Data Pipelines: Knowledge of best practices for data engineering, including Change Data Capture (CDC), slowly changing dimensions, and incremental loading.

• Engineering Standards: Skilled in code reviews, data quality testing, validation frameworks, and data governance (lineage, metadata management, and classification).

• Architecture Delivery: Ability to translate a Data Architect’s high-level vision and business requirements into scalable, working data solutions.

• Technical Ownership: Capable of balancing infrastructure performance, cost, and reliability with business logic and KPI definitions.

• Collaboration & Problem Solving: Comfortable leading design discussions, mentoring junior engineers, and driving root-cause analysis when technical issues arise.

• Work Management: Experience working in an Agile environment (using Jira), collaborating within a small team, and proactively communicating progress or blockers to management.

• Tools: Proficient with data catalog platforms and the Microsoft Office suite (Teams, Outlook, Excel).

• Regularly required to sit, stand, and walk in an office environment.

• Requires continuous use of computers, phones, and general office equipment.