VP, Data Engineering

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.

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

• 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

• 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

• 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

• 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
• Run the Data track of the Builder Bootcamp — define the curriculum, set the graduation bar, make the calls
• Partner with product and engineering on AI feature data requirements — you are the upstream dependency for almost everything

• Define data governance policies for AI‑consumed data: lineage, access control, PII handling, audit trails
• Own compliance requirements relevant to financial data in an ERP context — SOC 2, data residency, retention policies
• Build the observability layer:
  Open Telemetry, Weights & Biases, or equivalent for embedding quality and retrieval performance

• You have built and led a data engineering team before — you know how to hire, structure, and technically lead a team that ships production data systems
• Knowledge graph or MDM at scale: you have designed entity resolution, survivorship rules, and ontologies for complex relational domains — not just prototyped them
• AI/ML platform or LLMOps experience: you have operated embedding pipelines, vector stores, and LLM‑integrated data systems in production — you understand latency, cost, and quality trade‑offs
• You think in systems: schema design, retrieval architecture, and data contracts are your native language
• You are comfortable in ambiguity — greenfield means no existing patterns to follow and no team to hand things off to on day one

• Production RAG pipelines over structured or financial data — you have gone beyond demos and operated retrieval systems with real precision/recall requirements
• ERP, financial, or supply chain data domain — you understand what makes a General Ledger different from a web analytics event stream
• Modern data stack depth: dbt, Airflow,…