AI Analytics Engineer hybrid

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

3+ years in an Analytics Engineering, Data Engineering, or BI Engineering role delivering production dbt models at scale, 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