Operations Data Analyst

Monarch Landscape Companies

Monarch Landscape Companies is a family of successful landscape brands in eight states across the United States. We are a values-based learning organization committed to being the best place to work as a landscape professional. At Monarch Landscape Companies, your achievements determine your rewards, your abilities define your success, and your professionalism leads to autonomy!

Monarch Landscape Companies is seeking a highly technical and business-focused Data Analyst to join the team at our corporate headquarters in downtown Los Angeles. This is a full-time, on-site role dedicated to compiling, analyzing, and presenting operational data to support strategic decision-making and drive performance across the organization.

The ideal candidate will be passionate about data, comfortable navigating both modern and legacy systems, and able to translate complex datasets into actionable insights that support labor planning, revenue tracking, backlog visibility, and operational throughput. This role will also help advance Monarch's data capabilities by developing scalable solutions that reduce manual reporting, improve data quality, and create more standardized and reliable processes across the organization.

• Bachelor's degree in Data Analytics, Statistics, Business Intelligence, Computer Science, Information Systems, or a related field.
• 3+ years of experience in data analysis, preferably in operations, field services, or landscape maintenance.
• Expert-level Excel skills and comfort working with legacy systems and flat-file data.
• Working experience with Python and SQL, with the ability to develop queries, transform data, automate processes, and support data integration.
• Experience with financial and labor planning data, including budget vs. actuals and forecast modeling.
• Strong understanding of data transformation, data quality, and reporting processes.
• Strong communication skills and ability to work cross-functionally with technical and non-technical stakeholders.

• Familiarity with landscape maintenance operations or ancillary services.
• Experience with backlog management and labor optimization.
• Experience building automated reporting workflows or data pipelines.
• Experience working with multiple source systems and integrating disparate datasets.
• Knowledge of data engineering concepts, including ETL/ELT processes, data modeling, and data governance.
• Ability to work independently and collaboratively in a fast‑paced, data‑driven environment.
• Exposure to customer compliance dashboards, SOW repositories, and CSA score correlation tools.

• Aggregate, structure, and reconcile data from multiple internal operating systems.
• Build recurring and ad hoc reports that support labor planning, revenue tracking, backlog visibility, and throughput analysis.
• Develop workarounds, integrations, and automated processes for legacy systems and flat files to ensure complete and accurate data views.
• Learn and adapt to legacy platforms and data environments; prior experience is not required, but a willingness to learn is essential.
• Identify opportunities to replace manual reporting processes with standardized, automated, and repeatable solutions.

• Advanced use of pivot tables, Power Query, Power Pivot, and data modeling.
• Complex formula writing including INDEX/MATCH, XLOOKUP, array formulas, and nested logic.
• Experience with macros, VBA scripting, and automation techniques.
• Ability to build interactive dashboards, scenario models, and multi-tab reporting workbooks with structured formatting.

• Develop and maintain advanced capabilities in Python and SQL to support data analysis, transformation, automation, and integration across multiple systems.
• Write and optimize SQL queries to extract, join, validate, and analyze data from multiple sources.
• Use Python to automate recurring reporting, data preparation, validation, and workflow processes.
• Develop reusable scripts and tools that reduce manual data manipulation and improve reporting efficiency.
• Apply Python and SQL to identify data quality issues, reconcile datasets, and support scalable analytical solutions.
• Continuously expand technical capabilities to improve the organization's ability to move from manual analysis toward automated and standardized data processes.

• Experience with Power BI, including building dashboards, data…