Principal Software Engineer

Responsibilities

• What You Will Do Define and Champion the Architectural Roadmap: Architect the strategic evolution of existing source data pipelines to an ELT model of data ingestion, ensuring high efficiency, real-time capabilities, and cross-organizational adoption.
• Establish Data Architecture Standards: Lead the definition of architectural patterns for cleanly separating source-aligned data products from aggregate data products, enforcing domain separation, robust governance, and security across the entire data mesh.
• Drive Agentic First Data Product Strategy: Set the technical vision and standards for architecting, developing, and maintaining data products specifically optimized for consumption by autonomous AI Agents and Machine Learning models.
• Lead Technical Governance and Metadata Strategy: Establish best practices for richly decorating data products with metadata to support seamless knowledge transfer, mass adoption, and the responsible application of Machine Learning and AI Agents , including metadata specifically for defining agent capabilities and tool use.
• Oversee Compliance and Responsible Data Use: Define the strategy for tagging and classifying data assets to ensure they are used responsibly throughout the organization, architecting and implementing organization-wide solutions for masking or restricting access to meet global compliance standards.
• Cultivate Engineering Excellence: Mentor senior engineers, champion software engineering best practices, and drive improvements to the code release process to support CI/CD and a high-velocity InnerSource collaboration model.
• Drive Discoverability and Integration: Architect the data product catalog and integration strategy, ensuring data products are registered, easily discoverable, and seamlessly join with all other business data products using unified identifiers and keys.
• Establish Data Integrity Frameworks: Design and lead the implementation of automated, resilient, and proactive data quality testing and monitoring frameworks to guarantee data integrity for all business-critical applications and AI model training at scale.
• Lead AI Agent Deployment and Scaling: Serve as the strategic leader and subject matter expert for Agentic First Development , defining the methodology for building, deploying, and monitoring high-reliability, autonomous AI Agents and microservices, focusing on planning, tool integration, fault tolerance, and ultra-low latency.
• Proven track record in designing and deploying cloud-native data warehousing or data lake solutions at an enterprise scale (e.g., Snowflake, Databricks, BigQuery, S3/MinIO).