• Structure and manipulate databases (internal and external) using appropriate analysis tools; • Fix code issues and data-related problems; • Develop and maintain dashboards and reports to facilitate monitoring of operational performance; • Perform exploratory data analysis; • Analyze and interpret data using statistical tools and techniques, identifying trends and opportunities for process improvement; • Develop and present reports to business areas and managers to support decision-making; • Ensure data quality by collaborating with the IT team to guarantee data integrity and accuracy; engage, question, and propose improvements so that, together with the IT team, the data used in analyses are reliable; • Comply with company policies/procedures/standards related to the organization, work processes, and implemented management systems; • Perform other tasks related to the role and work procedures as required.

• Own cross-functional analytics projects. Partner with Operations, Sales, Finance, Product, Member Experience, leadership, and other teams to understand business problems, define requirements, analyze data, and deliver durable solutions. • Build trusted data models. Design, build, test, document, and maintain dbt models that transform raw source data into reliable, reusable, analytics-ready datasets. • Work deeply in BigQuery. Use advanced SQL in BigQuery to investigate complex questions, optimize queries, validate data, create reusable logic, and support production-grade reporting. • Support and improve the BI layer. Build and maintain dashboards, reports, semantic definitions, and self-service analytics experiences in Omni and/or Power BI. • Improve metric consistency. Help define business metrics, reconcile conflicting numbers, document assumptions, and ensure stakeholders understand what data means and how to use it. • Monitor and troubleshoot data quality. Work with Fivetran-fed data and downstream dbt/BI assets to identify pipeline issues, data freshness problems, schema changes, schema changes, source system inconsistencies, and reporting defects. • Translate business ambiguity into data solutions. Lead stakeholder discovery, ask sharp questions, clarify tradeoffs, and convert business needs into practical data requirements and deliverables. • Enable better decision-making. Deliver insights and reporting that help teams improve operational performance, sales effectiveness, member experience, financial visibility, and leadership decision-making. • Use AI tools thoughtfully and safely. Use Claude and other AI tools to accelerate analysis, SQL iteration, documentation, test generation, workflow automation, and stakeholder support while validating outputs and protecting data quality. • Raise the bar for the Data team. Contribute to naming conventions, documentation standards, QA practices, metric governance, dbt structure, BI usability, and overall trust in the data ecosystem. • Other duties as assigned.

Role Description We are seeking a Senior Utilities Data Analyst to join our Water Asset Management team and own the GIS application-development side of the practice. This role involves: - Designing Survey123 collection forms from scratch. - Developing Operations Dashboards and Experience Builder apps for client-facing visualization. - Writing custom integrations against the ArcGIS API for Python and JavaScript. - Driving how M&C ships its analytics products and mentoring the analysts who consume them. - Contributing to modern AI integrations that are becoming part of every utility data stack. Qualifications - Bachelor’s degree in Geography/GIS, Data Science, Computer Science, Engineering, or related field. - 5–7+ years of professional experience in GIS development, data engineering, or utility analytics — at least 3 of those in a hands-on GIS development role. - Demonstrated portfolio of deployed Survey123 forms, Experience Builder apps, or ArcGIS API integrations. - Strong analytical and problem-solving skills with the ability to interpret complex data and deliver meaningful insights. - Excellent written and verbal communication skills; comfortable engaging with clients and cross-functional teams. - Highly organized, self-motivated, and able to manage multiple tasks in a remote environment. - Valid driver’s license and acceptable motor vehicle and criminal record. Requirements - Production-grade Python — modular, tested, automated workflows for data ingestion, transformation, and analysis (Pandas, SQLAlchemy, HTTP libraries, Matplotlib / Plotly). - Advanced SQL — schema design, query optimization, indexing strategy, and integration with operational data stores. - ArcGIS Pro: Advanced spatial analysis, custom geoprocessing tools, ModelBuilder, ArcPy scripting. - Survey123: Design and deploy custom field collection forms — conditional logic, dynamic content, integration with downstream systems. - Experience Builder & Operations Dashboards: Develop client-facing apps from scratch — data widget configuration, custom theming, embedded analytics. - ArcGIS API for Python & JavaScript: Build custom integrations, automation scripts, and web applications that extend the Esri platform. - Experience connecting LLMs, ML services, or other modern tooling into Esri workflows (or strong interest in doing so). Benefits - Employee Stock Ownership Plan (ESOP): All employees are owners & benefit from profits earned. - Competitive pay: Paid holidays, bereavement, and parental, medical, and military leave. - Multiple office locations to work from: Stick close to home or travel for a change of scenery. - Growth opportunities & training: Grow confidently in your career with our mentoring & training options. - Professional development: Tuition reimbursement, early career professional program, online courses & more. - Work that makes a difference: See the direct impact your work has on our communities. - Collaborative, supportive team: People to help you solve problems, cheer successes & encourage you along the way.

• You sit between the business (Product Owner, stakeholders) and the engineering team. • About 30% of your time is practical data work: validating that CDP capabilities behave as specified, exploring data before specs are written, testing hypotheses for new capabilities, building shared gold layer models that consumer teams use, and helping those teams understand what data is available. • It is not business reporting or analytics built for individual brands. • The other 70% is Systems Analysis: turning what you find into specs engineers can build from, owning data quality from symptom to root cause, raising what isn’t right before it ships, and shaping the semantic layer so other business lines can find and trust the data. • Four things define the systems analysis side: Turn business intent into specs that engineers can build from. Talk to stakeholders, figure out what they actually need, and write it down. Each spec covers source schema, target model, transformation logic, business glossary, schedule, data quality checks, consumer, and use case. • Stay reachable through development. Engineers will have questions. Find and fix data quality problems at the root, alongside the engineering function. • Trace lineage, identify the cause, work with the data source owner on the fix, check downstream impact, and close the loop. • Confirm what’s delivered does what we said it would. Validate against the spec you wrote. When the data isn’t right, you raise it before it ships. • Make data usable beyond the team that asked for it. Contribute to the semantic layer, data descriptions, and shared definitions so other business lines can find what they need, understand what it means, and trust it without going through you each time. • You work on the Data Platform layer: CDP build, identity resolution, data quality, semantic definitions, and governance. Business analytics on top of the platform, such as dashboards and performance reports for specific brands, is produced by analyst teams aligned to those brands. Your job is to make sure those teams can do theirs: trustworthy data, clear definitions, fast issue resolution.