Data engineer

MincaAI; Monterrey; Hybrid; MXN 550,000 - 850,000; Full time

Benefits offered by this job

Impactful data models

Work with major Mexican insurers

Fast-moving team

Remote within Mexico

Job summary

MincaAI in Mexico is seeking a Data Engineer to turn messy client data into clean, structured data for AI workflows. You’ll own data across deployments for Auto Fleet, Property & Casualty, and Life, working with insurers on integrations and on-prem constraints.

You’ll build ingestion pipelines, enforce data quality, and collaborate with client IT teams to streamline onboarding for new deployments while using OpenShift and cloud/on-prem setups.

Qualifications

• 3+ years as a Data Engineer or Backend Engineer with heavy data work.
• Strong SQL and PostgreSQL data modeling, performance, migrations.
• Python for ETL/ELT pipelines.
• Experience integrating messy, heterogeneous sources (legacy databases, Excel/CSV, APIs).
• Daily use of AI coding tools (Claude Code, Cursor). This is non-negotiable.
• Professional English and native or fluent Spanish.
• Ownership: you take a fuzzy problem and ship a working solution without waiting for specs.

Responsibilities

• Map and model client databases into MincaAI’s canonical data model.
• Build and maintain ingestion and transformation pipelines from client core systems, files and APIs.
• Define data quality rules, validation and monitoring, and own data reliability in production.
• Work directly with client IT teams on extractions, integrations and on-premises constraints (OpenShift).
• Make client onboarding repeatable so each new deployment is faster than the last.

Skills

SQL

PostgreSQL

Python

ETL pipelines

Data integration

AI coding tools

English

Spanish

Ownership

Tools

OpenShift

AWS

Docker

Redis

dbt

Airflow

Job description

Data Engineer - Insurance AI (Mexico, Remote/Hybrid)

About MincaAI

MincaAI automates the core operations of insurers: underwriting, policy issuance, endorsements, collections and claims. We combine LLM reasoning with deterministic rules engines and deploy in the cloud or on-premises at some of Mexico’s leading insurers. We’re about 30+ people, growing fast, and we ship to production every week.

The role

Every new client brings its own data mess: legacy core systems, Excel files, PDFs, inconsistent catalogs and years of manual workarounds. You’ll turn that into clean, structured and reliable data our AI workflows can run on. You’ll be the reference for client data across our deployments (Auto Fleet, Property & Casualty, Life).

What you’ll do

• Map and model client databases (policies, vehicles, coverages, claims, collections) into MincaAI’s canonical data model
• Build and maintain ingestion and transformation pipelines from client core systems, files and APIs
• Define data quality rules, validation and monitoring, and own data reliability in production
• Work directly with client IT teams on extractions, integrations and on-premises constraints (OpenShift)
• Make client onboarding repeatable so each new deployment is faster than the last

Must-haves

• 3+ years as a Data Engineer or Backend Engineer with heavy data work
• Strong SQL and PostgreSQL (data modeling, performance, migrations)
• Python for ETL/ELT pipelines
• Experience integrating messy, heterogeneous sources (legacy databases, Excel/CSV, APIs)
• Daily use of AI coding tools (Claude Code, Cursor). This is non-negotiable.
• Professional English and native or fluent Spanish
• Ownership: you take a fuzzy problem and ship a working solution without waiting for specs

Nice-to-haves

• Insurance, banking or fintech data (policies, claims, catalogs)
• AWS (RDS, S3, SQS), Docker, Redis
• dbt, Airflow or similar orchestration
• Vector search and embeddings, fuzzy matching, entity resolution

What we offer

• Real impact: your data models become the foundation of MincaAI’s insurance AI infrastructure
• Production work with major Mexican insurers from day one
• Fast-moving, low-bureaucracy team using AI-native tooling across the board
• Competitive monthly fee (contractor), with performance bonuses tied to objectives
• Location: Mexico City preferred (occasional on-site work with clients); open to remote within Mexico