Analytics Engineer

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 ModelingData source ownership: Design and maintain the staging and intermediate dbt model layers for financial, operational, and asset-management data replicated from SAP (ERP) into SnowflakeSource-system partnership: Collaborate with SAP and integration teams to understand table structures, key relationships, and business rules translating them into well-documented, testable dbt modelsReliability & integrity: Implement incremental load strategies, referential integrity tests, null checks, and accepted-value constraints for all SAP-sourced modelsRevenue & Asset Performance ModelingData model ownership: Own the dbt models supporting power plant revenue calculations and budget variance analysisAsset performance marts: Build operational data marts integrating real-time and historical generation data, availability metrics, and O&M cost data for renewable asset evaluationBusiness alignment: Work closely with trading and risk teams to validate that margin and performance models reflect how the business measures and reports financial outcomesSemantic Layer & LLM-Ready Data ModelsSemantic model development: Build and maintain Snowflake semantic models (definitions that allow LLMs and BI tools to query business metrics without ad-hoc SQLMachine-readable design: Structure mart-layer models with consistent grain, clear naming conventions, and rich metadata so that AI-assisted querying returns accurate, interpretable resultsMetric governance: Define and document business metrics, dimensions, and hierarchies in a format that is both human- and machine-readable, in close partnership with Data EngineersHex Dashboard Data FoundationMart 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 stakeholdersPerformance optimization: Optimize mart models for Hex query patterns wide denormalized tables, pre-aggregated datasets, and semantic model hooks targeting sub-second dashboard response timesSelf-service enablement: Partner with analysts on Hex workbook design, providing clean, documented datasets and advising on best practices for self-service analyticsData Quality & GovernanceTesting frameworks: Implement comprehensive dbt tests (generic and data tests, singular tests, dbt-expectations) with clear severity thresholdsStandards & lineage: Establish and enforce naming conventions, data lineage documentation, and metadata standards across all analytical data assetsDocumentation: Define and maintain the data dictionary and mart-level documentation so both technical and business users can self-serve on definitions and model logicAI-Assisted DevelopmentLLM 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 outputResponsible 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 environmentsCross-Functional Collaboration & EnablementStakeholder engagement: Conduct data model walkthroughs and requirement workshops with business stakeholders across Finance, Accounting, Asset Management, Operations and Maintenance, Trading, and Credit risk teamsEngineering partnership: Partner with Data Engineers on pipeline design and source-layer contracts; with Data Scientists and Analysts on feature-engineering-ready datasetsTraining & onboarding: Create documentation, data dictionaries, and training materials that enable analysts to use data marts and Hex workbooks independentlySkills/Experience: Experience 3+ years in an Analytics Engineering, Data Engineering, or BI Engineering role delivering production dbt models at scaleEducationBachelors degree in Computer Science, Engineering, Mathematics, or a related quantitative fielddbtProficiency in dbt Core/Cloud staging/intermediate/mart layer patterns, incremental models, macros, generic & singular tests, documentation, and packages (dbt-utils, dbt-expectations) SnowflakeWorking knowledge of Snowflake: query optimization, result caching, clustering, time travel, and cost-aware model designSQLAdvanced SQL proficiency window functions, CTEs, recursive queries, and query performance tuningPythonPython proficiency for custom test development, scripting, and automationBI / DashboardingExperience building analytical datasets that serve BI tools; Hex experience strongly preferredVersion Control & CI/CDGit workflows, code review, and CI/CD for dbt projects; GitLab CI or Azure DevOps experience preferredAI ToolsPractical, critical use of LLM coding assistants (Claude Code, ChatGPT Codex, Copilot) with awareness of data privacy and prompt injection risks in production