7 Cim Group Security Jobs Hiring Near You

Job Summary:
CIM Group is a community-focused real estate and infrastructure owner, operator, lender, and developer. They are seeking a Senior Information Architect responsible for designing and maintaining scalable data models that support analytical and operational needs across their extensive portfolio. The role involves collaboration with various business stakeholders to ensure data requirements are met and establishing data modeling standards across the organization.
Responsibilities:
• Partners extensively with business stakeholders across Fund Accounting, FP&A, Global Client Group, and Investments teams to understand data requirements, pain points, and use cases.
• Translate complex business requirements into robust data models, asking questions first before proposing solutions to ensure alignment with actual business needs.
• Collaborate with data analysts, data scientists, MLOps engineers, and application developers to understand technical requirements and ensure models support downstream use cases.
• Build trust and influence across teams by demonstrating business value and explaining technical constraints in accessible terms.
• Design and develop conceptual, logical, and physical data models for various data initiatives, including data warehouses, data lakes, operational data stores, and transactional systems.
• Specialize in designing highly optimized dimensional models (star schemas, snowflake schemas) for analytical reporting and business intelligence applications.
• Apply various data modeling techniques as appropriate: Dimensional Modeling (Kimball), 3NF (Inmon), Data Vault, and NoSQL modeling patterns.
• Design and implement medallion architecture (bronze/silver/gold) patterns within the Databricks Lakehouse, establishing standards where none currently exist.
• Optimize data models leveraging Delta Lake features including ACID transactions, time travel, schema evolution, Z-ordering, and liquid clustering.
• Design partitioning strategies that balance query performance with file management, avoiding over-partitioning while enabling partition pruning.
• Implement Unity Catalog namespace hierarchy (catalog, schema, table) for multi-domain, multi-environment data organization and governance.
• Collaborate with MLOps engineers on data models that support ML feature stores and GenAI/RAG applications.
• Design and maintain relational database schemas for both operational and analytical workloads within Snowflake and other RDBMS, ensuring integration with Databricks Lakehouse.
• Design and optimize data structures for NoSQL databases (e.g., MongoDB), considering document structures, indexing, and query patterns for specific application needs.
• Develop frameworks for deciding when data belongs in Databricks Lakehouse vs. Snowflake vs. MongoDB based on workload characteristics and use cases.
• Provide input and recommendations on query optimization, indexing strategies, and data partitioning based on data model design.
• Diagnose and resolve performance issues including data skew, small files problems, and inefficient join strategies in Spark/Databricks environments.
• Collaborate with database administrators and data engineers on OPTIMIZE, VACUUM, and ANALYZE strategies for Delta tables.
• Work closely with Data Engineers to ensure data models are efficiently implemented and align with ETL/ELT processes using Auto Loader, Delta Live Tables, or traditional Spark jobs.
• Provide guidance on data mapping, transformation rules, schema evolution handling, and data loading strategies.
• Design slowly changing dimension (SCD) patterns using Delta Lake MERGE operations and Change Data Feed for downstream propagation.
• Establish and enforce data modeling standards, naming conventions, metadata management, and data governance policies—building these foundations where they do not currently exist.
• Implement row-level and column-level security patterns using Unity Catalog for sensitive fund and investor data.
• Design and maintain data lineage tracking from source systems through bronze/silver/gold layers to final reports.
• Contribute to the development and maintenance of a comprehensive data dictionary and metadata repository.
• Create and maintain detailed data model documentation, including data dictionaries, entity-relationship diagrams (ERDs), data flow diagrams, and data lineage.
• Document Lakehouse design patterns, medallion architecture implementations, and platform-specific best practices for team knowledge sharing.
• Contribute to data quality initiatives by identifying potential data quality issues at the modeling stage and collaborating on solutions.
Qualifications:
Required:
• Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or a related field.
• 10+ years of dedicated experience as a Data Modeler or Data Architect.
• Extensive experience with various data modeling techniques: Dimensional Modeling (Kimball), 3NF (Inmon), Data Vault, and NoSQL modeling.
• Strong expertise in SQL and experience with advanced SQL concepts for data analysis and modeling validation.
• Proficiency with data modeling tools (e.g., ER/Studio, Erwin, DataGrip, SQL Developer, or similar).
• Hands-on experience designing and implementing medallion architecture (bronze/silver/gold) in Databricks Lakehouse environments.
• Deep understanding of Delta Lake features: ACID transactions, time travel, schema evolution, Z-ordering, liquid clustering, and table maintenance.
• Experience with Unity Catalog for data governance, access control, and lineage tracking.
• Understanding Spark optimization, partitioning strategies, and performance tuning for large-scale data processing.
• Familiarity with Databricks SQL Warehouses, Delta Live Tables, and data pipeline patterns.
• Proven experience with Databricks/Delta Lake technologies - Experience modeling data within a data Lakehouse environment (Required).
• Solid understanding of Azure Cloud Platform: Azure Data Lake Storage, Azure Databricks.
• Experience with data ingestion concepts: Kafka, Azure Event Hubs, CDC, APIs and their impact on data structure and modeling.
Preferred:
• Prior experience within financial services, private equity, or alternative investments sectors.
• Experience building data architecture standards and practices from scratch in entrepreneurial environments.
• Understanding investment data structures: fund hierarchies, investor allocations, NAV calculations, capital calls/distributions.
• Experience supporting ML/AI use cases including feature engineering and data models for GenAI/RAG applications.
• Databricks Certified Data Engineer Associate or Professional.
• Microsoft Certified: Azure Data Engineer Associate.
• Microsoft Certified: Azure Enterprise Data Analyst Associate.
• TOGAF Certification (for enterprise architecture alignment).
Company:
CIM is a community-focused real estate and infrastructure owner, operator, lender and developer. Founded in 1994, the company is headquartered in Los Angeles, USA, with a team of 501-1000 employees. The company is currently Late Stage.