VP, Data Engineering

The Mission

Most data engineering roles are about moving data from A to B. This one is about making 20 years of complex, relational ERP data legible to AI agents — so they can reason over financial transactions, inventory movements, and supply chain events without hallucinating.

ECI is rebuilding how enterprise software is built and operated using an AI-native model. The data layer is the foundation everything else runs on. Without a world-class context engine, the agents are guessing. You are the person who makes sure they never have to.

This is a greenfield mandate. You will hire the team, choose the stack, define the architecture, and own the outcome. The CTO is your only direct stakeholder.

What You’ll Own

You are not supporting the AI initiative. You are building the infrastructure without which it cannot exist.

Context Architecture & Retrieval

Design and own the retrieval systems that allow AI agents to reason over ERP data with zero hallucinations

Build and scale the vector infrastructure — pgvector, Qdrant, or equivalent — with production-grade embedding and reranking pipelines

Own the hybrid search strategy: semantic retrieval layered on top of SQL-scoped financial data

Drive context window optimization — packing the most relevant financial 'truth' into each LLM call efficiently

Knowledge Graph & MDM

Lead the Master Data Management strategy — golden record survivorship, identity resolution, entity deduplication across ERP entities

Build the knowledge graph that maps relationships between Vendors, Purchase Orders, Invoices, GL Entries, and Inventory so agents understand meaning, not just rows

Own the semantic layer: translate a 500-table legacy schema into a structured, LLM-readable ontology

Define data quality standards and automated validation pipelines that enforce them continuously

Data Platform & Infrastructure

Build the core data platform from scratch: ingestion, transformation, storage, and serving layers

Own the modern data stack — dbt, Airflow or equivalent, Postgres/SQL Server — with an AI-augmented workflow throughout

Implement data-centric evals: 'Judge Agents' that verify AI output against ground truth SQL

Build synthetic data generation pipelines that produce high-fidelity, relationally consistent ERP data for agent training and testing

Builder Data Track

Own the Data Builder squad: hire, develop, and hold the team to Builder-level output standards

Partner with the Dev and QA Builder leads to ensure data systems are the right interface for agentic tool-calling