Data Applications & Analytics Analyst

LBA Logistics | LBA Properties•Irvine, CA

4d•$75,000 - $90,000•Onsite

About The Position

LBA Logistics & LBA Properties (“LBA”) is a full-service real estate investment and management company headquartered in Irvine, CA. LBA has a national industrial portfolio in all the major markets throughout the U.S. as well as a diverse office portfolio on the West Coast. LBA is a privately held company founded in 1991 with approximately 250 employees. LBA operates several closed-end real estate investment funds. LBA is hiring a Data Applications & Analytics Analyst to support the business applications that power our commercial real estate operations — and the data and reporting that flow out of them. This role sits close to the business. You will partner with users across leasing, accounting, asset management, operations, and finance to keep core systems running smoothly, troubleshoot data and process issues, and serve as a bridge between business teams and IT. Reporting, integrations, and AI / automation work are part of the role as well, but the primary focus is keeping the business applications working well for the people who rely on them every day. You will report to the Data Applications & AI Manager and work closely with business teams, IT, vendors, and external consultants. We are looking for someone with a solid grounding in SQL, reporting, and business applications, who is comfortable operating between technical and business teams. You don’t need to be a deep engineer — but you should be hands-on, detail-oriented, naturally curious, and capable of investigating data and process issues without constant direction.

Requirements

Hands-on experience in data analytics, business intelligence, data applications, system integration, or a closely related role.
Solid skills working with relational databases — joins, aggregations, validation queries, troubleshooting, and source-to-target reconciliation.
Working experience with a modern BI / dashboarding tool — report development, basic data modeling, filters, measures, and testing.
Exposure to cloud data platforms, data warehouses, lakehouses, or modern data environments.
Strong spreadsheet skills, including reconciling data across multiple sources.
Ability to translate business requirements into clear data, reporting, or application tasks.
Strong attention to detail and willingness to investigate data discrepancies independently.
Comfort working between technical teams and non-technical business users.
Strong written communication, documentation, organization, and follow-through.

Nice To Haves

Basic scripting experience for data processing, validation, or automation.
Experience working with operational and financial systems used in commercial real estate.
Background in commercial real estate, asset management, accounting, property operations, or finance.
Exposure to APIs, file-based ingestion, data migration, or system integration projects.
Familiarity with data governance, access control, row-level security, semantic models, or data quality frameworks.
Genuine interest in AI, automation, large language models, and practical business applications.
Experience with budget ownership, forecasting, procurement, and vendor management for end-user technology
Background in real estate, financial services, or professional services firms
Prior experience supporting executive users and office-based workplaces

Responsibilities

Serve as a primary point of contact for business users with application questions, support needs, or data issues.
Respond to day-to-day data and configuration requests across the company’s operational and business systems.
Build deep working knowledge of how data flows through each system — how it is entered, approved, maintained, and consumed.
Partner with business teams to scope, test, and roll out application enhancements, configuration changes, and workflow improvements.
Support user access reviews, role assignments, and permission updates across business applications.
Help triage reporting and application issues by tracing the root cause to process, source data, integration, access, or reporting logic.
Participate in user testing for system integrations, data migrations, releases, and workflow changes.
Document business processes, system workflows, and standard operating procedures so knowledge is shared and reusable.
Translate technical issues into clear, plain-language updates for business users.
Contribute to data integration work across the company’s cloud data platform, BI tools, and broader system landscape.
Help build, test, and monitor pipelines that move source data into centralized reporting environments.
Maintain tables, lakehouse structures, warehouse objects, and validation layers that support reporting-ready datasets.
Investigate refresh failures, missing records, schema changes, access problems, and data quality exceptions.
Coordinate with vendors and consultants to test deliverables and validate technical outputs.
Work with business users to gather requirements and turn them into clear data and reporting needs.
Contribute to dashboards, semantic models, validation reports, and operational reporting tools.
Build and test measures, calculations, filters, and data models so dashboards are accurate and easy to use.
Reconcile report outputs against source systems, Excel files, and business expectations.
Improve dashboard usability through better field organization, labeling, visuals, and consistent business logic.
Run recurring data quality checks — reconciliation, duplicate detection, missing values, and source-to-report validation.
Document data definitions, transformation logic, system mappings, and reporting assumptions.
Flag process gaps that cause inconsistent data and partner with stakeholders to resolve them.
Help establish repeatable validation processes, exception reports, and lightweight data quality controls.
Keep documentation current so reporting logic can be understood, reviewed, and reused.
Support internal AI and automation efforts focused on document analysis, data search, and reporting productivity.
Help evaluate and implement AI-enabled tools where they meaningfully improve validation, reporting, or analysis.
Document AI-related processes, user guidance, limitations, and governance considerations.
Spot opportunities to reduce manual reporting work and make recurring processes more repeatable.