# Senior AI Data Engineer | LATAM at Cadre AI

Cadre AI is hiring a Senior AI Data Engineer | LATAM for a remote role open to applicants in Colombia. Level rates it Builds AI ●●●●; you can [apply on Cadre AI](https://jobsbylevel.com/go/4d9edc16-1e62-48cf-a63c-8d3d69029c90?utm_medium=ai-agent).

AI Level 4, AI centrality 94 out of 100. Remote (Colombia - Remote).

## Details

- Company: [Cadre AI](https://jobsbylevel.com/companies/cadre-ai)
- AI level: AI Level 4 (score 94 out of 100)
- Location: Remote (Colombia - Remote)
- Posted: November 6, 2026
- Apply: https://jobsbylevel.com/go/4d9edc16-1e62-48cf-a63c-8d3d69029c90?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=senior-ai-data-engineer-latam-at-cadre-ai-4d5718

## Prepare for this job

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## Description

About Cadre AI
Cadre AI is an AI strategy and integration firm that builds production AI systems for B2B companies in private equity, wholesale lending, real estate, and SaaS. We do not build decks about what AI could do. We ship systems that move revenue, compress costs, and automate the work that used to take entire teams.
The Role
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, and 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.
What You’ll Do
Lakehouse and Pipeline Engineering
Design and build medallion (bronze, silver, gold) lakehouses on
Databricks
(
Delta Lake
,
Unity Catalog
,
Lakeflow Spark Declarative Pipelines
) and
Microsoft Fabric
(
OneLake
,
Data Factory
), and on
Snowflake
where a client already runs it.
Own ingestion from ERPs, CRMs, and SaaS platforms using platform-native connectors (
Lakeflow Connect
,
Fabric Data Factory
),
Fivetran
, or
dlt
, including change data capture and schema drift handling.
Model and transform data with
dbt
, orchestrate it with
Apache Airflow
,
Dagster
, or
Lakeflow Jobs
, and ship it through CI/CD with
GitHub Actions
and
Declarative Automation Bundles
(formerly Databricks Asset Bundles).
Build data quality checks and data contracts into every pipeline so downstream agents never consume silent errors.
Run agentic data engineering safely in client Databricks repositories: set up coding agents (
Claude Code
,
Codex
) with shared skills, policies, and guardrails, using
Omnigent
where it is available, and accelerate legacy-to-Databricks migrations with
Genie Migrate
.
Retrieval Systems: RAG and GraphRAG
Build production RAG pipelines end to end: document parsing and OCR, chunking, embeddings, hybrid retrieval (BM25 plus dense vectors), and cross-encoder reranking.
Design GraphRAG systems that extract entities and relationships into a knowledge graph, using frameworks such as
Microsoft GraphRAG
or
LightRAG
on graph stores such as
Neo4j
, for questions that span documents, systems, and multiple entity hops.
Choose the right retrieval architecture for each use case, from hybrid search with reranking to graph-based and agentic retrieval, and justify the cost and maintenance burden of each to the client.
Operate vector, full-text, and hybrid search infrastructure (
Databricks AI Search
, formerly Vector Search;
Azure AI Search
;
OpenSearch
;
pgvector
), including incremental indexing, permission-aware retrieval, and citations back to the source.
Build and deploy retrieval-backed agents on
Databricks Agent Bricks
, using its code-first tooling (
Agent Bricks CLI
, Agent Runtime on
Databricks Apps
) and governing model access through
Unity AI Gateway
.
Semantic Layers and Business Ontologies
Translate how a client defines a customer, an order, or an invoice into governed metric and entity definitions using
dbt Semantic Layer
(
MetricFlow
),
Power BI
semantic models,
Cube
,
Databricks
metric views, or
Snowflake
semantic views.
Lead data readiness assessments: inventory source systems, map fields and business logic, and document gaps and remediation steps before engineering starts.
Expose governed data to
Claude
and other agents through
MCP
, so

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Source: https://jobsbylevel.com/jobs/senior-ai-data-engineer-latam-at-cadre-ai-4d5718

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Level. https://jobsbylevel.com/jobs/senior-ai-data-engineer-latam-at-cadre-ai-4d5718.

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