AI Data Engineer

Experlogix

About The Position

We are building an AI Transformation program that automates real work across the company – support, onboarding, finance, sales operations – with measurable results. As our first AI Data Engineer you build the foundations that make that possible: the data pipelines, integrations, and agentic workflows that turn AI from demos into production systems, plus the measurement layer that proves what they deliver. You report directly to the Director of AI Transformation and work with every department in the company.

Requirements

4+ years building data pipelines and integrations in production – Python and/or C#, SQL, REST APIs; our stack is Azure Data Factory, Azure Functions, Blob Storage, and DuckDB
Hands-on experience building agentic solutions with LLM APIs (Azure AI Foundry, OpenAI, Anthropic or similar) – RAG, structured output, tool-calling, agentic frameworks – and testing/evaluating what they produce
An AI-native way of working: agentic coding tools (Claude Code, Cursor, Antigravity, GitHub Copilot or similar) are part of your daily workflow, and you can articulate where they accelerate you and where they mislead
Working proficiency in Power BI: data modeling, DAX, report design
Software engineering discipline: version control, code review, CI/CD, monitoring – you build systems that run unattended
Strong communication with non-technical colleagues; comfortable turning a messy process description into a technical design
Pragmatism: you ship a working 80% solution and iterate, and you flag when automation is the wrong answer

Nice To Haves

Experience in B2B SaaS operations (support, onboarding, CS tooling ecosystems)
Copilot Studio / Power Platform, or experience extending SaaS-embedded AI features
Familiarity with privacy-aware data handling (GDPR)
Dutch language skills (if NL-based)

Responsibilities

Design, build, and run agentic workflows and process automations end-to-end: extract and prepare data from source systems (CRM, ticketing, finance, product), orchestrate Azure AI Foundry models in agentic frameworks (durable Azure Functions) with tool-calling and retrieval, write results back into systems of record, and wrap everything in evaluation, monitoring, and guardrails so it keeps working after launch.
Build and maintain the pipelines and integrations the AI portfolio depends on – and the baseline/impact measurement data (hours saved, quality metrics) the program reports to the board.
Maintain and extend our Power BI reporting: semantic models, report building, and helping business users answer their own questions.
Join discovery workshops with departments, assess technical feasibility of automation candidates, and influence the decision per case: build custom, configure Copilot Studio, lean on SaaS-embedded AI, or skip.