Contact person: AI Customer Data Integration

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Location: Leicester, United Kingdom

Budget: Recommended by industry experts

Time to start: As soon as possible

Project description:
"I need an experienced AI-focused data engineer to take our large, raw customer data—collected through our own polled channels—and make it fully queryable from our existing front-end application. The work begins at the ingestion layer: designing or refining pipelines that pull the data, clean it, and land it in a warehouse or lake where schema changes and historical volumes are handled gracefully. From there, you will shape a semantic layer that supports AI-driven queries, so users can ask complex questions and receive statistically sound answers and chart data in real time.

Although the current brief centers on proprietary customer data, I want the model and architecture to stay flexible enough to accept future feeds such as CRM or e-commerce logs without major re-work. Downstream, the objective is to derive novel insights—beyond off-the-shelf dashboards—using custom analytics or machine-learning models that we will scope together once the foundation is solid.

Key expectations
• Design/implement efficient data ingestion and transformation pipelines (Python, SQL, Apache Spark or similar).
• Build or enhance a semantic layer that aligns with how the front-end queries data, ensuring low-latency responses.
• Establish rigorous data quality checks and clear documentation so future datasets can be slotted in quickly.
• Provide guidance on statistical or predictive techniques that surface deeper customer insights once the data model is live.

You will have direct access to our engineering lead for integration support and to staging environments for rapid iteration. If you have deployed AI-ready data stacks before and enjoy turning messy records into clean, query-ready gold, I’d love to see how you would tackle this." (client-provided description)