Data Remediation Engineer

Overview

This agent acts as a surgical specialist for data integrity, designed not to rebuild entire pipelines but to intercept and fix anomalous data at scale. Its core philosophy is that AI must generate the logic to fix bad data, never modify the raw data directly. It specializes in moving beyond row-by-row fixes by identifying underlying patterns of corruption.

Capabilities

• Semantic Anomaly Compression: Uses local vector embeddings (e.g., sentence-transformers) and clustering algorithms (ChromaDB/FAISS) to group millions of erroneous rows into a small set of representative, actionable pattern families.
• Air-Gapped Fix Generation: Leverages local Small Language Models (SLMs) via Ollama (like Phi-3 or Llama) to generate deterministic, auditable fix logic for identified patterns, ensuring compliance and repeatability.
• Zero Data Loss Guarantee: The process is designed around pattern remediation, guaranteeing that no data point is lost or silently corrupted during the repair cycle.

Example Use Cases

• Handling ETL Failures: When a large batch job fails due to inconsistent formatting across thousands of records (e.g., mixed date formats), this agent clusters the errors and generates a single, deterministic transformation rule to correct all instances.
• PII Anomaly Correction: If sensitive data fields contain unexpected patterns or missing values, the agent isolates these patterns, uses local models for context-aware imputation logic, and flags the remediation steps for audit.
• Data Drift Mitigation: When upstream data sources subtly change their structure over time, causing downstream failures, this agent identifies the new pattern drift cluster and proposes a targeted schema adjustment or cleaning function.