Job Description
Job Title: Analytics Engineer
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Work Mode: Hybrid
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Location: Scottsdale AZ 85251
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Contract: 12 months+ (Will be ext possibility)
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Job Description:
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We are seeking an Analytics Engineer to own the semantic layer and data mart strategy that connects our raw operational and financial data to the business teams who depend on it. A significant portion of your work will focus on modeling data replicated from SAP, our financial and other operational platforms into Snowflake building clean, tested, and well-documented dbt models that serve Hex dashboards and Snowflake semantic models queryable by LLMs.
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You are comfortable using AI coding assistants (Claude Code, ChatGPT Codex, GitHub Copilot) to accelerate data model development, and you apply critical judgment to every line of AI-generated output. You understand that speed and quality are not in conflict when LLM tooling is used responsibly.
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SAP-to-Snowflake Data Modeling
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Data source ownership: Design and maintain the staging and intermediate dbt model layers for financial, operational, and asset-management data replicated from SAP (ERP) into Snowflake
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Source-system partnership: Collaborate with SAP and integration teams to understand table structures, key relationships, and business rules translating them into well-documented, testable dbt models
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Reliability & integrity: Implement incremental load strategies, referential integrity tests, null checks, and accepted-value constraints for all SAP-sourced models
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Revenue & Asset Performance Modeling
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Data model ownership: Own the dbt models supporting power plant revenue calculations and budget variance analysis
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Asset performance marts: Build operational data marts integrating real-time and historical generation data, availability metrics, and O&M cost data for renewable asset evaluation
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Business alignment: Work closely with trading and risk teams to validate that margin and performance models reflect how the business measures and reports financial outcomes
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Semantic Layer & LLM-Ready Data Models
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Semantic model development: Build and maintain Snowflake semantic models (definitions that allow LLMs and BI tools to query business metrics without ad-hoc SQL
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Machine-readable design: Structure mart-layer models with consistent grain, clear naming conventions, and rich metadata so that AI-assisted querying returns accurate, interpretable results
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Metric governance: Define and document business metrics, dimensions, and hierarchies in a format that is both human- and machine-readable, in close partnership with Data Engineers
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Hex Dashboard Data Foundation
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Mart development: Build and maintain the data mart layer that serves Hex workbooks and dashboards consumed by Finance and Accounting teams, Asset managers, Energy traders, and executive stakeholders
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Performance optimization: Optimize mart models for Hex query patterns wide denormalized tables, pre-aggregated datasets, and semantic model hooks targeting sub-second dashboard response times
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Self-service enablement: Partner with analysts on Hex workbook design, providing clean, documented datasets and advising on best practices for self-service analytics
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Data Quality & Governance
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Testing frameworks: Implement comprehensive dbt tests (generic and data tests, singular tests, dbt-expectations) with clear severity thresholds
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Standards & lineage: Establish and enforce naming conventions, data lineage documentation, and metadata standards across all analytical data assets
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Documentation: Define and maintain the data dictionary and mart-level documentation so both technical and business users can self-serve on definitions and model logic
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AI-Assisted Development
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LLM productivity: Use AI coding assistants (Claude Code, ChatGPT Codex, GitHub Copilot etc.) to accelerate model scaffolding, SQL generation, test creation, and documentation while applying rigorous review to all AI-generated output
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Responsible AI practices: Participate in establishing team guidelines for secure, privacy-aware use of LLM coding tools, including managing data privacy boundaries and prompt injection risks in production environments
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Cross-Functional Collaboration & Enablement
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Stakeholder engagement: Conduct data model walkthroughs and requirement workshops with business stakeholders across Finance, Accounting, Asset Management, Operations and Maintenance, Trading, and Credit risk teams
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Engineering partnership: Partner with Data Engineers on pipeline design and source-layer contracts; with Data Scientists and Analysts on feature-engineering-ready datasets
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Training & onboarding: Create documentation, data dictionaries, and training materials that enable analysts to use data marts and Hex workbooks independently
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Skills/Experience:
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Experience
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- 3+ years in an Analytics Engineering, Data Engineering, or BI Engineering role delivering production dbt models at scale
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Education
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Bachelors degree in Computer Science, Engineering, Mathematics, or a related quantitative field
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dbt
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- Proficiency in dbt Core/Cloud staging/intermediate/mart layer patterns, incremental models, macros, generic & singular tests, documentation, and packages (dbt-utils, dbt-expectations)
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Snowflake
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- Working knowledge of Snowflake: query optimization, result caching, clustering, time travel, and cost-aware model design
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SQL
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- Advanced SQL proficiency window functions, CTEs, recursive queries, and query performance tuning
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Python
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- Python proficiency for custom test development, scripting, and automation
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BI / Dashboarding
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- Experience building analytical datasets that serve BI tools; Hex experience strongly preferred
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Version Control & CI/CD
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- Git workflows, code review, and CI/CD for dbt projects; GitLab CI or Azure DevOps experience preferred
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AI Tools
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- Practical, critical use of LLM coding assistants (Claude Code, ChatGPT Codex, Copilot) with awareness of data privacy and prompt injection risks in production
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