Analytics Engineer

Role:: Analytics Engineer
Location: Scottsdale, AZ Hybrid

Contract Long Term
Any Visa

Top Must-Haves:

• Strong hands-on dbt / dbt Core
• Strong Snowflake experience
• Excellent data modeling skills; raw/source data ? business-ready marts
• Experience with semantic views / semantic models
• Excellent communication + requirements gathering; must work directly with SAP and business SMEs
• Self-motivated, detail-oriented, able to independently drive work

Job Description:

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.

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.

SAP-to-Snowflake Data Modeling
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
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
Reliability & integrity: Implement incremental load strategies, referential integrity tests, null checks, and accepted-value constraints for all SAP-sourced models

Revenue & Asset Performance Modeling
Data model ownership: Own the dbt models supporting power plant revenue calculations and budget variance analysis
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
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

Semantic Layer & LLM-Ready Data Models
Semantic model development: Build and maintain Snowflake semantic models (definitions that allow LLMs and BI tools to query business metrics without ad-hoc SQL
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
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

Hex Dashboard Data Foundation
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
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
Self-service enablement: Partner with analysts on Hex workbook design, providing clean, documented datasets and advising on best practices for self-service analytics

Data Quality & Governance
Testing frameworks: Implement comprehensive dbt tests (generic and data tests, singular tests, dbt-expectations) with clear severity thresholds
Standards & lineage: Establish and enforce naming conventions, data lineage documentation, and metadata standards across all analytical data assets
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

AI-Assisted Development
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
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

Cross-Functional Collaboration & Enablement
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
Engineering partnership: Partner with Data Engineers on pipeline design and source-layer contracts; with Data Scientists and Analysts on feature-engineering-ready datasets
Training & onboarding: Create documentation, data dictionaries, and training materials that enable analysts to use data marts and Hex workbooks independently

Skills/Experience:
Area
Requirement
Experience
3+ years in an Analytics Engineering, Data Engineering, or BI Engineering role delivering production dbt models at scale

Education
Bachelors degree in Computer Science, Engineering, Mathematics, or a related quantitative field

dbt
Proficiency in dbt Core/Cloud staging/intermediate/mart layer patterns, incremental models, macros, generic & singular tests, documentation, and packages (dbt-utils, dbt-expectations)

Snowflake
Working knowledge of Snowflake: query optimization, result caching, clustering, time travel, and cost-aware model design

SQL
Advanced SQL proficiency window functions, CTEs, recursive queries, and query performance tuning

Python
Python proficiency for custom test development, scripting, and automation

BI / Dashboarding
Experience building analytical datasets that serve BI tools; Hex experience strongly preferred

Version Control & CI/CD
Git workflows, code review, and CI/CD for dbt projects; GitLab CI or Azure DevOps experience preferred

AI Tools
Practical, critical use of LLM coding assistants (Claude Code, ChatGPT Codex, Copilot) with awareness of data privacy and prompt injection risks in production

 

Required Skills:

Desired Skills:

Nice to Have

     dbt Analytics Engineer certification

     Experience building Snowflake semantic models (Cortex Analyst) or dbt Semantic Layer / MetricFlow definitions for LLM or BI consumption

     Experience with or exposure to Dagster our production orchestration platform for understanding and working with upstream pipeline dependencies

     Energy, utilities, or financial services industry experience

     Knowledge of Energy Gross Margin concepts, power generation metrics, or energy trading/market data

     Experience with Snowflake Cortex (LLM functions, Cortex Analyst, ML-powered features)

     Familiarity with data governance and regulatory compliance frameworks: SOX, GDPR, CCPA

     Experience with dbt Cloud jobs, environments, and orchestration