About Monster Energy:

Forget about blending in. That's not our style. We're the risk takers, the trailblazers, the gamechangers. We're not perfect and we don't pretend to be. We're raw, unfiltered, and a bit unconventional. Our drive is just like our athletes, unrivaled. The power is in your hands to define what success looks like and where you want to take your career. It's not just about what we do, but about who we become along the way. We are much more than a brand here. We are a way of life, a mindset. Join us.

A Day in the Life:

In the position of AI Data Engineer, you will engineer the data that Monster's artificial intelligence (AI) agents and copilots depend on, owning AI data readiness from source to answer. Assess and remediate data for specific AI use cases; build the retrieval pipelines, embeddings, and search indexes that ground Microsoft Copilot, Copilot Studio, Agentforce, and custom OpenAI and Claude-based agents; and maintain the semantic layer that links related records across systems (for example, a Salesforce account to its SAP financials and Power BI reporting). Working in Microsoft Fabric, Azure, and Databricks with the AI Architect, Sr. AI Solution Engineer, and AI Engineer, make enterprise data AI-ready, measurable, and trustworthy, so every AI answer can be traced to its source.

The Impact You'll Make:

• Retrieval (RAG) Data Pipelines — Build and operate ingestion, parsing, chunking, enrichment, embedding, and vector / hybrid indexing pipelines (e.g., Azure AI Search, Databricks Vector Search) that ground Copilot, Copilot Studio, Agentforce, and custom agents; enforce permission-aware retrieval that respects source-system security.
• Semantic Layer & Grounding Context — Design and maintain Power BI / Fabric semantic models, business glossary, and entity relationships (e.g., customer, product, and distributor across Salesforce, SAP, and BI) that give AI agents and Fabric data agents a shared, accurate understanding of the business.
• AI Data Readiness Assessment & Remediation — Assess source data for each AI use case (coverage, quality, freshness, sensitivity, and bias); score AI readiness; and drive remediation with source owners before AI build begins.
• AI Data Access via MCP & APIs — Expose curated, zero-copy data in Microsoft Fabric / OneLake and Databricks to AI agents through governed interfaces (e.g., MCP servers, SQL endpoints, APIs) with least-privilege access.
• Retrieval Quality, Observability & Feedback — Monitor index freshness, data drift, and retrieval quality (relevance, groundedness, citation accuracy); feed agent logs and user feedback back into data fixes; publish AI-readiness key performance indicators (KPIs) in Power BI.
• Metadata, Lineage & AI Data Governance — Register AI data assets in Microsoft Purview or Collibra; apply sensitivity labels; maintain lineage from source to index, agent, and answer; partner with Legal, Privacy, and Security on personal data, retention, EMEA data residency, and AI regulatory requirements.
• Evaluation & Ground-Truth Datasets — Curate golden question-and-answer sets, labeled ground truth, and test data that measure AI accuracy; prepare data for fine-tuning or model customization when needed, including LLM-generated synthetic data with appropriate controls.
• LLM-Assisted Data Engineering & Standards — Use OpenAI and Claude models and AI coding tools (e.g., Cursor) to automate metadata enrichment, entity matching, documentation, and pipeline code; contribute AI data standards, templates, and playbooks.

Who You Are:

• Prefer a Bachelor's Degree in the field of Computer Science, Artificial Intelligence, Information Systems, Statistics, or related.
• 3-5 years of experience in data engineering (pipelines, data modeling, SQL, Spark), including work on Microsoft Azure / Fabric or Databricks
• 1-3 years of experience in building data pipelines specifically for AI or generative AI use cases (RAG, embeddings, vector search, semantic models, evaluation datasets)
• Computer Skills Desired: Languages / query: SQL (advanced), Python (data tooling, PySpark), Spark (intermediate)
• Platforms: Microsoft Fabric / OneLake (Data Factory, Lakehouse, Warehouse, data agents), Azure data services, Databricks, or equivalent
• AI data tooling: Azure AI Search and vector / hybrid search, OpenAI embedding models via Azure OpenAI, Azure AI Document Intelligence, LangChain or similar for ingestion, MCP servers for data access
• Models: OpenAI GPT-5.6 / GPT-6 and Anthropic Claude Sonnet 5.5 / Haiku 5.5 for enrichment, classification, and evaluation
• Metadata / catalog: Microsoft Purview, Collibra (or similar); Git for versioning
• Modeling / orchestration: dbt, Azure Data Factory / Fabric pipelines, or Airflow
• Dashboards for AI-readiness KPIs: Power BI
• AI-assisted development: Cursor, Claude Code, or GitHub Copilot
• Preferred Certifications: Microsoft Certified: Fabric Data Engineer Associate (DP-700), which replaces the retired Azure Data Engineer Associate (DP-203)
• Databricks Certified Data Engineer Associate or Generative AI Engineer Associate
• Microsoft AI-103: Developing AI Apps and Agents on Azure (desirable)
• Collibra or Microsoft Purview practitioner training / certification
• Additional Knowledge or Skills to be Successful in this role: Strong collaboration with the AI Architect, Sr. AI Solution Engineer, AI Engineer, data engineering, governance, and security teams; ability to turn business concepts into semantic models, entity relationships, and retrieval-ready data; treats retrieval quality and data trust as measurable outcomes