Data Engineer, AI

Cadastra

Engenheiro(a) de Dados & IA developing architectural solutions and frameworks for client growth. Collaborating across teams to enhance data and AI product value.

Posted 7/3/2026full-timeRemote • 🇧🇷 BrazilMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Data Lakehouse architecture and cloud computing, with strong capabilities in Python, SQL, and data orchestration. Proven ability to bridge technical and business teams, ensuring effective communication and collaboration while implementing best practices in data engineering.

Highest-signal resume keywords

Data Lakehouse ArchitecturePython ProgrammingCloud Computing (AWS, Azure, GCP)Data Orchestration (Airflow)ETL/ELT Processes

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Python ProgrammingSQL SkillsData Lakehouse ArchitectureETL/ELT ProcessesData Orchestration (Airflow)CI/CD (GitHub Actions, Cloud Build)Data GovernanceData Extraction via APIsData CleansingData Aggregation

Soft Skills

Communication SkillsCollaborationProactivityAutonomyProduct Mindset

Tools & Technologies

AWSAzureGCPAirflowGitHub ActionsCloud Build

Industry Keywords

Data EngineeringAI SolutionsAgile ProjectsData ProcessesData Pipelines

Tech Stack

Tools & technologies

AirflowAWSAzureCloudETLGoogle Cloud PlatformPythonSQL

About the role

Key responsibilities & impact

Propose and build: Design modern architectural solutions (focus on Data Lakehouse) and rapidly test new technologies.
Develop: Create efficient data processes and pipelines (ETL/ELT) using industry best practices.
Automate and Orchestrate: Implement automations in Cloud environments (GCP, AWS, or Azure), ensuring high performance, security, and data governance.
Operate with a Product Mindset: Ensure the data platform is scalable, flexible, and oriented to serve business areas with structured data and AI models.
Facilitate and Translate: Act as a bridge between technical and business teams, translating complex engineering and AI concepts for stakeholders in a clear and educational manner.
Collaborate: Support colleagues and teams in adopting industry best practices, serving as a technical reference.

Requirements

What you’ll need

Strong Python skills: Essential for developing Data Engineering and AI solutions.
Data Architecture: Solid experience with modern architectures, particularly Data Lakehouse.
Cloud Computing: Hands-on experience with cloud platforms (AWS, Azure, or GCP), focusing on managed data and AI services.
Orchestration and Pipelines: Experience with data orchestration tools (preferably Airflow).
Data Handling: Strong SQL skills and experience with ETL/ELT processes (data extraction via APIs, cleansing, deduplication, aggregation, and anonymization).
Best Practices: Experience with CI/CD (GitHub Actions / Cloud Build) and experience working in agile projects.
Autonomy and Proactivity (Builder): Hands-on, curious, and independent profile, able to propose solutions and validate hypotheses quickly.
Product Mindset: Ability to understand that the goal of engineering is not only the data but the product (data/AI) that addresses and solves other areas' needs.
Communication and Collaboration: Excellent communication skills to translate the "alphabet soup" of AI engineering for business teams, ensuring alignment and engagement.

Benefits

Comp & perks

Meal and food allowance on FLASH card 🥗
Home office stipend on FLASH card 💳
Health insurance 🩺
Dental insurance 🦷
Birthday day off + an amount credited to the FLASH card 🎉
Extended maternity and paternity leave 🍼
Profit sharing (PLR) 💰
Life insurance 🧡
Childcare assistance 👶
Referral bonus 💰
Transport voucher 🚍
Clude | Health platform 🩺
TotalPass (fitness/wellness) 🏋🏽‍♀️