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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing and maintaining scalable data pipelines using Databricks and Python, with a strong focus on data quality, performance, and reliability. Proficient in SQL and data modeling, with experience in building reusable code components for analytics and reporting.
Highest-signal resume keywords
Databricks Data Pipeline DevelopmentPython Data ProcessingSQL for Large-Scale DatasetsETL Pipeline ManagementData Quality Assurance
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 TransformationData ValidationData NormalizationData ModelingReusable Component DesignETL Pipeline BuildingData Ingestion WorkflowsKPI ConsolidationPerformance Optimization
Soft Skills
Attention to DetailCollaborationContinuous ImprovementWritten CommunicationVerbal Communication
Tools & Technologies
GitHubDatabricksSQLPython
Industry Keywords
Data EngineeringJourney AnalyticsScalable Data SolutionsAnalytics Datasets
Tech Stack
Tools & technologiesETLPythonSQL
About the role
Key responsibilities & impact- Build, maintain, and optimize scalable data solutions for Journey Analytics initiatives
- Maintain and automate existing GitHub code repositories
- Refactor legacy code for simpler maintenance, updates, and reuse
- Design and build modular, reusable code components
- Develop and manage automated Databricks data pipelines
- Consolidate KPIs, metrics, and attributes into standardized data structures
- Build and maintain scalable data models for journey analytics
- Ensure data quality, performance, and reliability across pipelines and analytics datasets
- Collaborate with analytics and engineering teams to improve data processes and architecture
Requirements
What you’ll need- Hands-on experience developing and maintaining data pipelines in Databricks
- Strong hands-on experience with Python for data processing and transformation
- Strong experience with SQL and large-scale datasets
- Experience in data validation, data transformation, and normalization
- Experience building and maintaining ETL pipelines and handling data ingestion workflows
- Strong attention to detail with a focus on data quality and reliability
- 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
- Excellent written and verbal English
- At least 5+ years of experience in data engineering or similar roles
Benefits
Comp & perks- Certifications in AWS, Databricks, and Snowflake
- Access to AI learning paths
- Study plans, courses, and additional certifications tailored to the role
- Access to Udemy Business
- English lessons
- Travel opportunities to attend industry conferences and meet clients
- Career development plans and mentorship programs
- Special day rewards for birthdays, work anniversaries, and other personal milestones
- Company-provided equipment
- Flexible working options
- Other benefits may vary according to location in LATAM