[Hiring] Senior AI Data Engineer @Go Cadre

Role Description

You will build the data foundations that make our clients’ AI systems trustworthy: the lakehouses that unify their ERPs and SaaS tools, the retrieval systems that ground agents in their documents and records, the semantic layers that give every metric one definition, and the evaluation datasets that prove an answer is right. Every agent, dashboard, and workflow Cadre ships sits on top of the data layer you design.

Your week moves between architecture and execution:

• Mapping a manufacturer’s ERP tables with their finance team one day.
• Building Databricks medallion layers or tuning a hybrid retrieval pipeline the next.

You thrive here if you:

• Keep up with retrieval research.
• Know why a random split leaks in a time series.
• Can explain a GraphRAG trade-off to a CFO in plain language.

You will work in client pods with AI engineers, solution architects, and AI strategists, and shape the reusable data and retrieval blocks every engagement starts from.

Qualifications

• 5+ years of data engineering experience, including at least one production lakehouse you designed and operated on Databricks or Microsoft Fabric with a medallion architecture; experience on both is a plus.
• A Master’s degree or PhD in Computer Science, Mathematics, Engineering, or a related quantitative field.
• Hands-on experience shipping RAG pipelines to production.
• Experience designing and running GraphRAG or knowledge-graph retrieval.
• Production experience orchestrating data pipelines with Apache Airflow or Dagster.
• Expert SQL and strong Python and PySpark skills.
• Experience authoring a semantic or metrics layer.
• Working ML fluency.
• Hands-on experience building agents on Databricks.
• Governance depth across Unity Catalog, Purview, or Snowflake RBAC.
• Client-facing consulting or forward-deployed experience.

Requirements

• Design and build medallion (bronze, silver, gold) lakehouses on Databricks, Microsoft Fabric, and Snowflake.
• Own ingestion from ERPs, CRMs, and SaaS platforms using platform-native connectors.
• Model and transform data with dbt, orchestrate it with Apache Airflow, Dagster, or Lakeflow Jobs.
• Build data quality checks and data contracts into every pipeline.
• Run agentic data engineering safely in client Databricks repositories.
• Build production RAG pipelines end to end.
• Design GraphRAG systems that extract entities and relationships into a knowledge graph.
• Operate vector, full-text, and hybrid search infrastructure.
• Build and deploy retrieval-backed agents on Databricks Agent Bricks.
• Translate how a client defines a customer, an order, or an invoice into governed metric and entity definitions.
• Lead data readiness assessments.
• Expose governed data to Claude and other agents through MCP.
• Build golden datasets, output and label pairs, and backtesting splits.
• Instrument retrieval and agent pipelines with dedicated tracing and evaluation tools.
• Monitor drift, freshness, and data gaps that degrade agents in production.
• Implement role-based access, row-level security, and masking.
• Record architecture decisions as ADRs in the client repository.

Benefits

• AI-native culture.
• Access to the frontier through partnerships with Anthropic and OpenAI.
• Upside with a bootstrapped, profitable, and fast-growing environment.
• No bureaucracy with a small, scrappy team and clear accountability.