> Markdown version of [/jobs/ext/2693010-ai-data-sme](https://www.wearedevelopers.com/jobs/ext/2693010-ai-data-sme). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI & Data SME - **Company:** Vings Technologies - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Temporary to permanent - **Skills:** Artificial Intelligence, BigQuery, Data Architecture, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Flow Control, Graph Database, Identity and Access Management, Python (Programming Language), Cloudera, Search Technologies, Microsoft SharePoint, SQL Databases, Google Cloud, Large Language Models, Snowflake, Microsoft Onedrive, Adobe, Virtual Agents, Data Pipelines, Servicenow, Databricks - **Published:** September 3, 2026 - **Apply:** https://www.dice.com/job-detail/06866ba3-ff74-43e6-8ef3-57382c5baff2 ## About the Role * Deep enterprise data engineering: pipelines, ETL/ELT, data modelling, and data quality at scale. * Hands-on with Google Cloud data stack: BigQuery, Vertex AI Search, AlloyDB/Cloud SQL, Dataflow/Dataproc. * Strong RAG design: embeddings, vector databases, chunking, hybrid/semantic search, grounding evaluation. * Experience integrating enterprise sources (Databricks, ServiceNow, Snowflake, SharePoint/OneDrive). * Proficiency in Python and SQL; solid grasp of GenAI/LLM data patterns. * Strong data governance, privacy (PII/DLP), and security knowledge. Preferred (Good-to-Have) Skills * Knowledge graphs and advanced retrieval (GraphRAG, corrective/self-RAG). * Snowflake / Databricks certifications; Google Professional Data Engineer. * Experience with AI evaluation frameworks and observability for data quality. * Prior high-tech / semiconductor domain exposure. Experience & Certifications * 9-12+ years in data engineering / data architecture with recent GenAI/RAG delivery (Tier 5-6). * Google Professional Data Engineer / relevant cloud data certification preferred. * Onsite (USA), aligned to hours and data-track stakeholder collaboration. ## Description Serves as the senior subject-matter expert bridging enterprise data engineering and AI grounding for the Agent Factory. Owns the strategy and design for extracting, normalizing, and grounding enterprise data (Databricks, ServiceNow, Snowflake, OneDrive, Adobe, PDFs) for high-quality, reliable AI agent consumption, and advises on data architecture, RAG quality, and AI data governance., * Define the enterprise data-to-AI strategy: ingestion, normalization, embeddings, vector stores, and RAG grounding for agents. * Architect and oversee data pipelines from source systems (Databricks, ServiceNow, Snowflake, OneDrive, Adobe, PDFs) into Google Cloud (BigQuery, Vertex AI Search, AlloyDB, Vector Search). * Own retrieval quality: chunking strategies, embedding models, hybrid search, and grounding accuracy for Finance/Marketing/HR agents. * Establish AI data governance: data privacy, PII controls, lineage, quality, and access management. * Advise architects and developers on data readiness, feature/knowledge design, and evaluation of grounded responses. * Guide model selection and cost/performance trade-offs for data-heavy agent workloads. * Act as a senior technical authority in stakeholder discussions with Google on data-track dependencies. * Mentor Data & AI Engineers and set standards for reusable data assets in the Agent Library. ## Related Videos - [Beyond GPT: Building Unified GenAI Platforms for the Enterprise of Tomorrow](https://www.wearedevelopers.com/videos/1525-beyond-gpt-building-unified-genai-platforms-for-the-enterprise-of-tomorrow) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [3x Performance: A Humbling Journey](https://www.wearedevelopers.com/videos/100165-3x-performance-a-humbling-journey) - [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai) - [Making Data Warehouses fast. A developer's story.](https://www.wearedevelopers.com/videos/302-making-data-warehouses-fast-a-developer-s-story) - [NoCode LiveCode: Leveraging AI Tools to Craft Fully Functional Apps!](https://www.wearedevelopers.com/videos/1169-nocode-livecode-leveraging-ai-tools-to-craft-fully-functional-apps) ## Related Articles - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Best US AI Conferences for CTOs in 2026: Build vs. Buy, Vendor Evaluation, and Peer Intelligence](https://www.wearedevelopers.com/magazine/736-best-us-ai-conferences-for-ctos-in-2026-build-vs-buy-vendor-evaluation-and-peer-intelligence) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)