Business Intelligence Specialist

Signorelli Company•The Woodlands, TX

About The Position

The Signorelli Company is a vertically integrated, diversified development company based in The Woodlands, TX. As one of the largest privately held development companies in Texas, Signorelli has more than 25 years of experience in all aspects of real estate development including retail, mixed-use, multifamily, office, healthcare, land development and single-family homes. As Signorelli Company's Business Intelligence Specialist, you'll architect and own the analytical foundation that powers decision-making across a $1B+ vertically integrated real estate development portfolio. This is a SQL- and data-model-first role where you'll design and maintain production-grade analytics logic within our cloud data warehouse, transforming data from ERP systems, FP&A platforms, CRM, and operational sources into trusted, AI-ready datasets that drive strategic decisions across residential, commercial, and mixed-use development projects. You'll work at the warehouse and modeling layer—not just building dashboards, but creating the reusable, well-structured analytical logic that serves multiple business units and downstream tools including Power BI, Vena, and our AI-powered knowledge platform. Your models become the source of truth that Finance, Operations, and Development teams rely on to manage active projects, forecast performance, and allocate capital.

Requirements

3–6 years of experience in business intelligence, analytics engineering, or data analytics roles
Advanced proficiency in SQL, including designing and maintaining reusable analytical logic in production environments
Experience owning analytical models in a cloud data warehouse (Snowflake preferred)
Experience working with data produced by Python-based pipelines or cloud-native jobs
Ability to design analytics systems used by multiple teams or business units
Experience designing or maintaining prompts for AI tools that interact with enterprise data
Experience working with CRM data (HubSpot preferred), FP&A systems, or financial reporting
Familiarity with Azure services supporting analytics workflows (e.g., Container Apps, Data Factory, storage)
Strong analytical and problem-solving skills.
Ability to translate complex technical concepts into clear business insights.
Excellent written and verbal communication skills.
Comfort working in a fast-paced, evolving environment.
Strong organizational skills with attention to detail.
Ability to build positive working relationships with technical and non-technical stakeholders.

Nice To Haves

Bachelor’s degree in Information Systems, Data Analytics, Computer Science, Finance, or a related field preferred.
Relevant analytics, data, or cloud certifications preferred.
Applicable software & hardware certifications, preferred.

Responsibilities

Partner with Finance, Operations, Development, and Shared Services to translate business questions into production-grade analytics solutions.
Own and maintain analytical data models within the cloud data warehouse, including schemas, views, and transformations that serve as the source of truth for reporting and analytics
Design, write, and maintain repeatable SQL logic that supports analytics models, dashboards, and AI-enabled tools in production environments
Ensure data produced by Python-based pipelines and Azure container workloads is validated, modeled, and analytics-ready for downstream consumption
Collaborate with a Data Scientist to align pipeline outputs, data contracts, and handoff points between engineering and analytics layers
Design and maintain Power BI datasets and semantic models that consume warehouse-layer logic rather than embedding business rules in reports
Troubleshoot data quality issues at the source, transformation, and model layer, and drive resolution through clear ownership and documentation
Own analytics use cases leveraging data from ERP, FP&A systems, CRM, and third-party platforms
Define and maintain documentation for analytical models, metrics, and reporting standards to ensure consistency and long-term maintainability
Own the analytics-facing integration of AI tools, ensuring data access aligns with governance standards and analytical intent
Participate in defining and maintaining internal AI usage policies, including guidance on prompt design, data access, and responsible use