DQP: Enterprise Data Quality Platform
Designing and building the core engine for a platform that unifies data pipelines, ML, profiling, validation, agents, and 300+ DQ tools.
Column-aware, cache-aware, graph-based execution.
The engine plans work per-column, deduplicates via content-hash caching, and runs asynchronously - turning complex DQ workflows into a live execution trace.
Enterprise-scale numbers.
What the platform actually does.
Anonymised summary of the engineering and product surface across engine, tools, ML, agents and integrations.
Problem
Enterprise data quality is fragmented - profiling, validation, cleaning, anomaly detection and reporting live in separate tools with no shared execution model.
Execution engine
A column-aware, cache-aware, graph-based engine that plans, deduplicates, and executes DQ operations across large tabular datasets asynchronously.
Tool library
300+ purpose-built tools spanning cleaning, standardisation, validation, risk analysis, issue detection and human-language insight generation.
ML & agents
Custom ML for anomaly detection and profiling, plus AI agents that assemble and execute data quality flows on top of the engine.
MCP integration
Platform capabilities exposed as callable tools via MCP, letting enterprise systems and LLMs invoke DQ operations from natural-language prompts.
Throughput
Thousands of rows per second depending on cleaning, profiling, and analysis scope - engineered for real enterprise data volumes.