Data Engineer Manager

Blend360

Data Engineer Manager responsible for leading a team of data engineers at an AI services provider. Overseeing the development of data solutions and architecture for Journey Analytics initiatives.

Posted 7/22/2026full-timeRemote • 🇨🇴 ColombiaSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive experience in Data Engineering, focusing on the development and management of scalable data pipelines and robust data modeling practices. Proven ability to lead teams, enforce coding standards, and drive continuous improvement in data processes and architecture.

Highest-signal resume keywords

Data EngineeringData Pipeline DevelopmentGitHub Version ControlData ModelingDatabricks

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringData Pipeline DevelopmentData ModelingLegacy Code RefactoringAutomated Data Pipelines

Soft Skills

Team LeadershipCross-Functional CollaborationContinuous Improvement

Tools & Technologies

DatabricksGitHubGenie

Industry Keywords

Data QualityData GovernanceScalabilityPerformanceAnalytics Use Cases

About the role

Key responsibilities & impact

Lead and mentor a team of data engineers, fostering best practices in coding, architecture, and data engineering standards.
Define and drive the technical strategy for Journey Analytics data platforms, ensuring scalability, maintainability, and performance.
Oversee the maintenance, optimization, and automation of code repositories in GitHub, ensuring high-quality and consistent development practices.
Guide the refactoring of legacy codebases to improve maintainability, scalability, and reusability across multiple use cases.
Drive the design and implementation of modular, reusable data components to support multiple journeys and reduce duplication.
Oversee the development and management of automated data pipelines in Databricks, ensuring reliability and scalability for downstream consumption.
Establish and enforce standards for scalable data modeling to support current and future analytics use cases.
Ensure data quality, governance, performance, and reliability across all data pipelines and datasets.
Partner with analytics, product, and engineering stakeholders to align data solutions with business needs and priorities.
Proactively identify risks, bottlenecks, and improvement opportunities, and drive mitigation strategies at a team and platform level.
Promote continuous improvement of data processes, documentation, and engineering practices.

Requirements

What you’ll need

7+ years of experience in Data Engineering.
Strong experience working with GitHub repositories and version control workflows.
Hands-on experience developing and maintaining data pipelines in Databricks.
Proven experience refactoring and maintaining legacy codebases.
Strong understanding of data modeling and reusable component design.
Experience building scalable data models for analytics and reporting use cases.
Strong focus on data quality, performance, and reliability.
Ability to work in cross-functional environments and contribute to continuous improvement.
Ability to work independently and take ownership of initiatives after receiving high-level direction, driving tasks forward with minimal supervision.
Experience using Genie (Databricks) (Plus).

Benefits

Comp & perks

Certifications in AWS (we are AWS Partners)
Access to AI learning paths to stay up to date with the latest technologies.
Study plans, courses, and additional certifications tailored to your role.
Access to Udemy Business, offering thousands of courses to boost your technical and soft skills.
English lessons to support your professional communication.
Travel opportunities to attend industry conferences and meet clients.
Career development plans and mentorship programs to help shape your path.
Special day rewards to celebrate birthdays, work anniversaries, and other personal milestones.
Company-provided equipment.
Flexible working options to help you strike the right balance.
Other benefits may vary according to your location in LATAM.