What The Role Is

We are seeking a Senior Analyst, Analytics Engineering to bridge the gap between business questions and trusted data. You’ll work directly with stakeholders, first and foremost our asset managers, along with our Commercial Analytics (CA) and Market Optimization and Trading (MOT) teams and partners in finance, accounting, procurement, and operations, to understand what they need, build the data models that answer it, and deliver the reports, dashboards, and light predictive analyses that put it to use.

The role’s initial workload is expected to be roughly one-third each: (1) revenue analytics and stakeholder reporting; (2) modeling SAP data across accounting, procurement, and operations and maintenance (O&M) to serve teams across the organization; and (3) monthly curtailment reporting in support of the Commercial Analytics manager. Priorities may shift with business needs. This is a hands-on, hybrid role (3 days in office) that combines data analysis (Python, data visualization, requirements gathering) with analytics engineering (SQL, dbt, version control, documentation) on a modern stack: Snowflake, dbt, Hex, Streamlit, and GitLab.

This role is suited to an analyst who has independently delivered reliable business reporting and analytical solutions, brings strong SQL, dbt, and Python skills, and wants to expand their ownership of data modeling and analytics engineering while working closely with business stakeholders.

This role is located in our Scottsdale, AZ office.

What You'll Be Doing

Requirements Gathering & Stakeholder Partnership

• Partner first and foremost with asset managers, and with Commercial Analytics (CA), Market Optimization and Trading (MOT), commercial, finance, accounting, procurement, and operations stakeholders to translate business questions into clear requirements, metric definitions, and deliverables
• Manage a backlog of reporting and analysis requests, prioritizing with your manager and communicating scope, timelines, and tradeoffs
• Present findings and recommendations to technical and non-technical audiences in clear, concise language

Revenue Analytics, Reporting & Data Visualization (~1/3)

• Develop analyses and reporting on revenue and related commercial and trading performance metrics, including variance and trend analysis
• Design, build, and maintain reporting dashboards and self-service data applications (e.g., Hex, Streamlit) that give stakeholders reliable, timely access to key metrics
• Apply sound visualization practices so that insights are clear, accurate, and actionable
• Support recurring and ad hoc reporting requests, including requests from senior management

SAP Data Modeling & Analytics Engineering (~1/3)

• Develop and maintain dbt models that transform raw data into well-structured, business-ready datasets (staging, intermediate, and mart layers)
• Model SAP data in Snowflake for accounting (e.g., GL, cost centers, WBS), procurement (e.g., purchase orders, invoices, vendors), and O&M (e.g., work orders, maintenance cost) reporting that serves teams across the organization; translate SAP structures into intuitive, documented analytical models
• Work with finance, accounting, procurement, and O&M subject-matter experts to define data mappings and reconciliation rules
• Write clear, performant SQL for Snowflake, following team conventions and Snowflake best practices
• Implement dbt data tests and source freshness checks to validate data quality and timeliness, including reconciliation of analytical models to SAP source totals
• Use Git and GitLab for version control and code review of analytics code

Curtailment Reporting & Commercial Analytics Support (~1/3)

• Apply established, site-specific curtailment methodologies to produce monthly curtailment reports for the wind, solar, and solar-plus-storage fleet, including reruns and backfills, and prepare reports for review and release by the Commercial Analytics manager
• Validate input data and results before release, including prediction error, missing or stuck values, timestamp coverage, and dispatch data
• Troubleshoot failed runs and improve the reliability, automation, and documentation of reporting scripts

Scripting & Light Predictive Analytics

• Use Python to automate data preparation, build analysis scripts, and prototype lightweight forecasting and statistical models (e.g., regression, simple time-series, trend and anomaly detection)
• Use approved AI coding tools to accelerate SQL and Python development, reviewing, testing, and validating all generated code and outputs before they reach production or stakeholders
• Partner with senior team members to move validated prototypes into maintained, documented workflows

Documentation & Data Governance

• Create and maintain model documentation, metric definitions, data dictionaries, and onboarding materials so that data assets are discoverable and understandable
• Follow Clearway data classification and access-control standards, handling sensitive commercial and financial data appropriately in all analyses, tools, and outputs
• Flag data quality issues early and work with data engineering and source-system owners to resolve root causes

What You'll Bring

• Experience and Education: Bachelor’s degree in Engineering, Computer Science, Data Science, Finance, Accounting, Economics, Business, or a related quantitative field, with 3+ years of relevant experience in data analysis, business intelligence, or analytics engineering, including a track record of independently delivering reliable analytical work
• SQL: Strong SQL skills, including joins, window functions, CTEs, and query optimization; experience with a cloud data warehouse (Snowflake preferred)
• dbt: Hands-on experience building and maintaining dbt models, including dbt data tests and documentation
• Data Modeling: Working knowledge of dimensional modeling and analytics-ready data structures
• Python: Proficiency in Python for data analysis and scripting (e.g., pandas, NumPy, Matplotlib, Plotly)
• Version Control: Hands-on experience with Git, including branching, pull/merge requests, and code review
• AI-Assisted Development: Ability to use approved AI coding tools (e.g., Claude Code, OpenAI Codex, Snowflake Cortex Code) responsibly, with appropriate code review, testing, validation of results, and protection of sensitive data
• Excel: Strong Excel skills for building, reviewing, and quality-checking report workbooks
• Data Visualization: Experience building dashboards and reports in a modern BI or visualization tool (e.g., Hex, Power BI, Tableau, Spotfire, or Streamlit)
• Requirements Gathering: Demonstrated ability to work with business stakeholders to clarify ambiguous requests and deliver the right outcome
• Documentation: Commitment to clear documentation of data models, metrics, and analyses
• Communication: Ability to explain technical concepts to non-technical audiences
• Work Style: Self-directed, detail-oriented, and comfortable managing multiple priorities; able to work in the Scottsdale office 3 days per week

What Would Be Nice

• Experience modeling data from SAP or a comparable ERP (e.g., Oracle, NetSuite, Workday) for accounting, procurement, or maintenance reporting
• Experience with high-frequency (e.g., 1- to 5-minute) time-series data or renewable operational data, such as SCADA or historian data, power curves, or availability calculations
• Familiarity with curtailment, PPA terms, ISO/RTO markets (e.g., CAISO, ERCOT), or LMP data
• Experience in the energy, utilities, or financial services industries, including energy markets, trading, asset performance, or O&M data
• Experience with revenue, financial, or commercial analytics
• dbt Analytics Engineering Certification
• Exposure to workflow orchestration tools (e.g., Dagster, Airflow)
• Experience with forecasting or predictive modeling (e.g., scikit-learn, statsmodels)
• Experience building data apps with Streamlit.

No visa sponsorship or any kind of sponsorship for employment authorization is available for this role, now or in the future.

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