Risk Data Engineer and Architect at Lincoln Financial supporting risk analytics through AWS data solutions. Building scalable data pipelines and collaborating with cross - functional teams.
About the role
- Data Architect leading the design and governance of data architecture for FFF Enterprises. Collaborating across teams to optimize data solutions and support analytics and reporting initiatives.
Responsibilities
- Lead the design and governance of our enterprise data architecture.
- Define and implement end-to-end data architecture standards, including data modeling, data integration, metadata management, and governance across the enterprise.
- Design scalable data models across Bronze, Silver, and Gold layers of a Databricks Lakehouse using Delta Lake and Unity Catalog and DLT.
- Collaborate with data engineers to optimize ingestion and transformation processes across streaming and batch pipelines.
- Establish canonical models and semantic layers that power downstream BI tools and self-service analytics.
- Define and enforce data quality, data lineage, and data security policies in coordination with governance teams.
- Work with business stakeholders, product managers, and analysts to translate analytical use cases into high-quality data models and architecture patterns.
- Provide architecture oversight and best practices guidance on data integration tools (e.g., Fivetran), data cataloging, and performance optimization.
- Review and approve physical and logical data model changes across teams to ensure consistency and maintain architectural integrity.
Requirements
- Bachelor’s Degree in a related field or four (4) years relevant experience in lieu of degree.
- Deep understanding of SAP ERP data models, especially core financials, logistics, and materials domains.
- Expertise in dimensional modeling, star/snowflake schemas, and modern data warehousing patterns.
- Proficiency in SQL and Python, with the ability to guide data engineers on best practices.
- Strong understanding of data governance, lineage, and security frameworks.
- Ability to communicate architectural concepts clearly to both technical and non-technical audiences.
- Must have seven (7) years [eleven (11) for non-degreed candidates] of experience in data architecture or data engineering, with a focus on enterprise-scale data.
- Strong hands-on experience with Databricks, including Delta Lake, Unity Catalog, and Lakehouse architecture.
- Proven experience in conceptual, logical, and physical data modeling using tools like ER/Studio, Erwin, Lucidchart, or dbt.
- Experience working in cloud environments such as Azure, AWS, or GCP.
Benefits
- Health insurance
- 401(k) matching
- Paid time off
- Professional development opportunities
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