Analytics Engineer

Motion Recruitment; Scottsdale (AZ); On-site; USD 120,000 - 170,000; Full time

Job summary

Motion Recruitment is seeking an Analytics Engineer to own the semantic layer and data mart strategy that connects SAP-replicated data to business teams relying on it. You will build clean, tested dbt models in Snowflake and design semantic models queryable by BI tools and LLMs.

You will leverage AI coding assistants to accelerate model development while maintaining rigorous review, ensuring speed and quality in production. Collaboration with SAP teams and data engineers is essential.

Qualifications

• 3+ years in analytics/data/BI engineering delivering production dbt models at scale.
• Bachelor's degree in Computer Science, Engineering, Mathematics, or related quantitative field.
• Proficiency in dbt Core/Cloud staging/intermediate/mart layer patterns, incremental models, tests, documentation.
• Working knowledge of Snowflake concepts and SQL performance tuning.
• Python proficiency for test development and automation.

Responsibilities

• Own the semantic layer and data mart strategy connecting SAP data to business teams.
• Build and test dbt models for SAP-replicated data in Snowflake.
• Define governance, metadata, and data dictionary for analytical assets.
• Collaborate with SAP, Data Engineers, and analysts on data marts and dashboards.
• Optimize data mart performance for Hex workbooks and finance dashboards.
• Enable self-service analytics through clean datasets.

Skills

dbt

Snowflake

SQL

Python

Git & CI/CD

AI tools usage

Education

Bachelor's degree

Tools

dbt-utils

dbt-expectations

GitLab CI

Azure DevOps

Job description

Contract: 12 months+ (Will be ext possibility)

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

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:

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
• 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