Principal Data Engineer

Siemens develops connected insights, AI-powered technology, and intelligent asset management solutions that help organizations improve maintenance, operations, and asset performance. The Principal Data Engineer will lead the design, governance, and evolution of enterprise Data Cloud capabilities supporting analytics, AI, reporting, and secure data sharing, while providing technical direction across Snowflake, dbt, Apache Superset, observability, lineage, and governance.

Responsibilities:

• Leading Data Cloud strategy, standards, and solution design across enterprise data, analytics, AI, and reporting initiatives
• Designing scalable data systems, including ingestion, transformation, logical and physical models, semantic layers, and governed consumption patterns
• Providing technical direction for Snowflake and dbt architecture, administration, performance, security, cost management, and release practices
• Architecting and supporting secure Data Share solutions, summary views, account provisioning, entitlement workflows, and product onboarding
• Leading Apache Superset architecture, administration, semantic modeling, dashboard enablement, performance optimization, and migration from legacy BI platforms
• Advancing data governance, metadata, privacy, anonymization, retention, access controls, quality monitoring, and compliance practices
• Driving platform reliability through observability, lineage, monitoring, alerting, incident readiness, and operational automation
• Supporting commercial and government cloud environments while partnering with security and infrastructure teams on compliant platform designs
• Translating manufacturing and government asset-management needs into durable data models for assets, facilities, infrastructure, maintenance, work management, inspections, lifecycle planning, reliability, and capital planning
• Mentoring engineers and collaborating across global product and delivery teams to move designs from concept through production

Requirements:

• Bachelor's degree in computer science, engineering, information systems, data engineering, or a related field, or equivalent practical experience
• 8+ years of progressive experience in data architecture, cloud data platforms, data engineering, or closely related roles, including ownership of enterprise-scale designs
• Demonstrated expertise with Snowflake, dbt, SQL, dimensional and normalized data modeling, data integration, and cloud-native data platform patterns
• Hands-on knowledge of Apache Superset, including enterprise deployment, administration, security, semantic datasets, dashboard development, performance tuning, and integration with cloud data warehouses
• Demonstrated expertise with manufacturing and asset-intensive data, including asset hierarchies, equipment, facilities, maintenance, work orders, reliability, inspections, inventory, and lifecycle information
• Demonstrated expertise with government or public-sector asset data, including infrastructure inventories, facilities, compliance, capital planning, budgeting, and governed reporting
• Experience designing data governance, metadata, lineage, privacy, access-control, retention, and anonymization capabilities
• Experience with platform observability and operational practices, including monitoring, alerting, solving, reliability, and production support
• Ability to communicate complex technical decisions clearly and influence partners across product, engineering, analytics, AI, security, and business teams
• Ability to work effectively across a globally distributed organization and lead through technical influence
• Qualified Applicants must be legally authorized for employment in the United States
• Qualified Applicants will not require employer- sponsored work authorization now or in the future for employment in the United States
• Experience modernizing analytics from Qlik or other legacy BI platforms to Apache Superset or comparable open analytics platforms
• Experience with AWS data services, GovCloud environments, Snowflake Streamlit, Grafana, OpenLineage, or similar technologies
• Knowledge of Salesforce account, opportunity, entitlement, and customer-product data integration patterns
• Experience supporting enterprise asset management, computerized maintenance management, facilities management, or asset performance management products
• Understanding of AI/ML data enablement, MLOps integration, agentic analytics, data catalogs, business glossaries, and governed self-service analytics
• Experience establishing architecture standards, design reviews, operating models, or technical roadmaps for Data Cloud platforms