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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates extensive experience in Data Engineering, focusing on the development and management of scalable data pipelines and models. Proven ability to lead teams, enforce best practices, and drive technical strategies for data platforms.
Highest-signal resume keywords
Data EngineeringData Pipeline DevelopmentGitHub Version ControlData ModelingDatabricks
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data Pipeline DevelopmentData ModelingLegacy Code RefactoringData Quality AssurancePerformance Optimization
Soft Skills
Team LeadershipCross-Functional CollaborationContinuous Improvement
Tools & Technologies
DatabricksGitHubGenie
Industry Keywords
Data Engineering StandardsScalable Data ModelsAutomated Data PipelinesData GovernanceAnalytics 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- 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.