VP, Data Engineering

The Mission

The role involves transforming 20 years of complex, relational ERP data into a format that allows AI agents to reason over financial transactions, inventory movements, and supply chain events without hallucinating. ECI is rebuilding enterprise software using an AI-native model, and this role is critical to ensuring the success of the project.

About the role

You are responsible for designing and owning the retrieval systems that allow AI agents to reason over ERP data with zero hallucinations. You will also lead the Master Data Management strategy, build the knowledge graph, and define data quality standards and automated validation pipelines.

Responsibilities

• 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
• 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
• 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
• 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
• 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

Requirements

• 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
• Think in systems: schema design, retrieval architecture, and data contracts are your native language
• Comfortable in ambiguity — greenfield means no existing patterns to follow and no team to hand things off to on day one
• Highly desirable: 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, Postgres, SQL Server — you have opinions about transformation layer design and know when to break the rules
• Experience working across time zones with an offshore engineering team (India context is a plus)

Stack

• Languages: Python, SQL (Postgres / SQL Server), TypeScript
• AI / Retrieval: OpenAI / Anthropic APIs, pgvector, Qdrant, LangChain / LangGraph
• Data Platform: dbt (AI-augmented), Apache Airflow, Docker
• Graph / MDM: Neo4j (primary), with open evaluation of alternatives
• Observability: Weights & Biases (embedding evals), OpenTelemetry, custom Judge Agents Infra
• AWS / GCP, Kubernetes, GitHub Actions

Archetypes

• The Data Alchemist — you believe data is only valuable when an AI can reason over it, and you spend time experimenting with embedding models and retrieval techniques to make that true
• The Manual Mapping Hater — if you have to map two schemas twice, you've already built an agent to do it for you
• Rigor over Hype — you know the difference between a vector search demo and a production-grade financial data engine; you care about Precision and Recall
• The Founding Mindset — you're energized by building from scratch, not managing existing systems, and you make decisions confidently without a playbook