VP Senior Data Engineer

CREA, LLC is a full-service low-income housing tax credit (LIHTC) syndicator forming long-term relationships with investors and developers that cultivate success and improve lives. With over 20 years in affordable housing, CREA has raised $14.1 billion - resulting in the formation of 106K+ homes within 1,122 communities across the country. Thanks to the contribution of over 130 employees, CREA continues to look for talented and passionate individuals who are excited about opportunities to grow with us into the future. 

Physical Presence: In-Office | Tue – Thu; Flexible | Mon & Fri

Open to: Boston, MA, or Indianapolis, IN

Reports to: Senior Vice President, Business Intelligence & Analysis

Position Summary:

We are seeking a VP / Senior Data Engineer with a minimum of 9 years of relevant experience; 10+ years is preferred. This hands-on technical leadership role is responsible for designing, modernizing, and managing the enterprise data platform across Microsoft Fabric, Power BI, Azure SQL, data integration, ETL/ELT, and warehouse solutions. The successful candidate will combine deep engineering expertise with strong data management, reporting, governance, and stakeholder leadership capabilities.

Primary Responsibilities:

Enterprise Data Platform & Architecture

• Lead the design, implementation, modernization, and support of scalable enterprise data platforms using Microsoft Azure and Microsoft Fabric.
• Define practical data architecture standards for ingestion, transformation, storage, semantic modeling, reporting, security, and lifecycle management.
• Design solutions using Fabric Data Lake, Warehouse, Data Factory, notebooks, pipelines, and semantic models based on business and technical requirements.
• Guide modernization and migration of on-premises SQL Server workloads and data integrations to Azure SQL and Microsoft Fabric.
• Evaluate platform capabilities, capacity needs, performance, reliability, and cost to support sustainable data services.

Power BI Reporting & Platform Management

• Lead Power BI report, dashboard, semantic model, and dataset development for executive, operational, and analytical reporting.
• Administer and govern Power BI and Fabric workspaces, deployment pipelines, access, sharing, refresh schedules, gateways, and environment promotion.
• Establish reporting standards for data modeling, DAX, Power Query, naming, documentation, certification, reuse, and lifecycle management.
• Implement row-level security, object-level security, workspace roles, and controlled distribution of business information.
• Monitor refresh reliability, query performance, capacity utilization, adoption, and report quality; troubleshoot and optimize issues.

Data Engineering, ETL/ELT & Integration

• Design, build, test, deploy, and maintain secure, reliable, and reusable ETL/ELT pipelines and data integrations.
• Create ingestion and transformation processes for structured, semi-structured, and unstructured data from cloud and on-premises sources.
• Use Microsoft Fabric Data Factory, Azure Data Factory, SQL, APIs, and other integration methods to support batch and near-real-time use cases.
• Implement orchestration, scheduling, dependency management, error handling, restorability, logging, monitoring, and operational documentation.
• Build reusable frameworks and automation to reduce manual data processing and improve delivery consistency.

Azure SQL, Warehouse & Data Modeling

• Design, develop, administer, and optimize solutions using Azure SQL Database, Azure SQL Managed Instance, SQL Server, and Fabric Warehouse.
• Apply relational, dimensional, star-schema, and semantic modeling techniques to support reporting, analytics, and business operations.
• Develop and optimize T-SQL, stored procedures, views, functions, indexes, and data transformation logic.
• Manage data warehouse and lakehouse structures, historization, incremental processing, partitioning, and performance optimization.
• Partner with application and infrastructure teams on secure connectivity, database migration, availability, backup, and recovery requirements.

Additional Responsibilities:

Data Management, Quality & Governance

• Establish and support data management practices covering ownership, stewardship, metadata, lineage, classification, retention, and access.
• Implement data quality controls, reconciliation, validation, monitoring, and issue-management processes across pipelines and reporting solutions.
• Define and maintain business and technical data definitions, source-to-target mappings, lineage documentation, and data catalogs.
• Support master and reference data management practices that improve consistency across systems and reporting.
• Champion data privacy, security, regulatory, audit, and governance requirements in partnership with cybersecurity and business stakeholders.

Technical Leadership & Stakeholder Partnership

• Provide hands-on technical leadership, architecture guidance, engineering standards, solution reviews, code reviews, and mentoring.
• Partner with business leaders and subject matter experts to translate reporting and analytics needs into prioritized, scalable data solutions.
• Communicate platform roadmaps, dependencies, risks, tradeoffs, and operational performance to technical and executive stakeholders.
• Coordinate work across employees, consultants, vendors, and business partners while maintaining accountability for solution quality.
• Create and maintain architecture diagrams, data models, technical specifications, runbooks, operating procedures, and support documentation.

Operations, Security & Continuous Improvement

• Monitor data platform availability, pipeline execution, refresh performance, storage, capacity, and service health across cloud and on-premises environments.
• Lead troubleshooting, root cause analysis, remediation, and continuous improvement for data and reporting incidents.
• Apply least privilege, managed identities, secrets management, encryption, private connectivity, and secure development practices.
• Support CI/CD, source control, testing, release management, and automated deployment for data solutions and Power BI content.
• Research and recommend improvements to analytics, warehouse, data virtualization, integration, automation, and AI-enabled data capabilities.