Database Engineer 3

Job Description
Job Summary:
DENSO North America Enterprise Solutions Group is transforming its enterprise analytics and business intelligence capabilities through a comprehensive Data Lakehouse platform built on Databricks. This strategic initiative involves migrating legacy data warehouses, reporting systems, and analytical workloads from on-premise platforms (SQL Server data warehouses, Oracle analytics, iSeries reporting) and current BI tools (DOMO, Qlik) into a unified, governed analytical platform. The organization manages complex analytical data flows, dimensional modeling, reporting datasets, and advanced analytics capabilities across multiple business domains.

The E3 Data Engineer role serves as a technical leader in building and maintaining enterprise-scale analytical data solutions. This position focuses on designing data warehouse architectures, implementing ETL/ELT processes, creating dimensional models, and enabling self-service analytics within the modern Data Lakehouse framework. The role bridges traditional data warehousing and business intelligence expertise with cutting-edge lakehouse technologies, working within established governance frameworks to deliver scalable, performant analytical solutions that support strategic business decision-making across the enterprise.

Data Lakehouse Role Responsibilities:
Based on project needs and organizational priorities, the E3 Data Engineer may be assigned to fulfill one or more of the following platform or domain roles:

Platform Role Capabilities:
Platform Engineer

• Build and maintain core platform infrastructure and data pipelines
• Support catalog automation and orchestration across domains
• Monitor job performance and compute resource usage
• Collaborate with Unity Catalog admins for infrastructure provisioning

Platform Data Scientist

• Conduct advanced analysis and modeling across domains using certified data
• Build and maintain ML models within the Databricks ML platform
• Share insights and collaborate with domain teams and governance leads

Release Manager

• Coordinate promotion of notebooks, jobs, and data objects between environments
• Oversee change control and deployment workflows
• Manage versioning and release calendar coordination
• Validate content before production deployment

Domain Role Capabilities:
Domain Editor

• Develop data transformations and ingestion logic for specific domains
• Write and maintain bronze/silver dataset pipelines
• Coordinate with domain stewards for data certification processes
• Maintain documentation and versioning of domain datasets

Primary Accountabilities:

• Lead data migration projects from legacy database systems (SQL Server, Oracle, DB2), DOMO, and other existing platforms to Databricks Data Lakehouse
• Design and implement medallion architecture (Bronze/Silver/Gold) following established governance standards
• Lead hybrid data architecture spanning on-premise and cloud systems across multiple database platforms
• Architect scalable ingestion pipelines using Databricks workflows, Delta Live Tables, and other platform-native tools
• Design enterprise-scale data transformation pipelines with emphasis on real-time and batch processing capabilities
• Collaborate with Domain Stewards and Approvers to ensure data certification and compliance with Unity Catalog RBAC
• Implement data quality frameworks and monitoring solutions across the lakehouse platform
• Mentor junior engineers and provide technical guidance on modern data platform best practices
• Evaluate and recommend new data sources for platform integration, including structured and unstructured data assessment
• Drive automation initiatives to reduce manual processes and improve operational efficiency
• Support and build replication and advanced data movement technologies between disparate systems
• Organize and coordinate with team members to resolve complex technical issues and project deliverables
• Utilize change management tools to document and gate all changes that could affect production systems
• Building advanced tools to automate data collection and processing workflows
• Utilize source control tools and best practices while developing enterprise-scale solutions
• Drive continuous learning initiatives and knowledge