Niche

Data Engineer

• Lead and manage a team of 3–5 data/software engineers executing the data product roadmap
• Oversee scalable, secure, and observable data pipelines and warehouse layers using Python, SQL, Snowflake, DBT, Airflow, and Kafka
• Manage sprint planning, estimation, standups, and retrospectives
• Collaborate with Analytics, Product, and Engineering on data requirements and business priorities
• Guide monitoring, troubleshooting, and production issue resolution
• Uphold testing, CI/CD, code reviews, and documentation practices
• Coach team members on technical execution, communication, and career development
• Remove process bottlenecks to improve velocity, quality, performance, and cost efficiency
• Participate in hiring and onboarding
• Review current data systems, pipelines, and platform architecture
• Drive roadmap accountability, data quality, pipeline reliability, platform scalability, and team performance
• Evolve team structure and operating model as business needs grow

Manager, Data Engineering

Analytics Engineer

• Architect, build, and scale enterprise data models, translating complex raw data into clean, business-ready datasets
• Drive data accuracy, documentation, observability, semantic consistency, and warehouse performance
• Lead the design and maintenance of modular dbt models, semantic layers, and BI views
• Partner with executive stakeholders to translate business strategy into analytics architecture, governance, and self-service capabilities
• Architect and maintain Kimball dimensional models, standardized metric definitions, warehouse transformation logic, and scalable BI datasets
• Design and govern production-grade models powering the enterprise semantic layer and AI/ML integrations
• Establish data quality, automated testing, lineage tracking, cost optimization, and alerting across dbt pipelines
• Refactor legacy business logic and fragmented query architectures into scalable semantic pipelines
• Drive data governance, metric standardization, secure access controls, and mentor junior and mid-level engineers
• Lead analytics platform workstreams, own domain models, conduct root-cause analysis, and shape the technical roadmap
• Report to the Manager, Data Engineering (Core)

Senior Analytics Engineer, Brazil, Argentina

• Build and support scalable data models that translate raw data into clean, business-ready datasets
• Ensure data accuracy, documentation, observability, semantic consistency, and performance
• Develop and maintain modular dbt models, semantic layers, and BI views for business analysts and executive stakeholders
• Understand evolving business trends, business logic, and cross-functional partnerships to embed context and governance in the data layer
• Develop, document, and maintain Kimball dimensional models, semantic definitions, warehouse transformation logic, and BI datasets
• Design, build, and maintain version-controlled data models supporting self-service analytics and reporting
• Ensure data quality and pipeline accuracy through automated dbt testing, lineage tracking, performance tuning, and proactive alerting
• Improve complex business logic and fragmented queries by standardizing analyst requests into documented semantic models
• Integrate data governance, metric standardization, documentation, single-source-of-truth definitions, and secure data access
• Collaborate with data team members and stakeholders; participate in standups, planning, and retrospectives
• Troubleshoot data logic issues and modeling errors and participate in analytics support activities
• Identify improvements to data transformation processes and semantic definitions
• Contribute to the analytics platform, dbt models, semantic layers, and BI reporting frameworks
• Independently own analytics engineering workstreams and modeling roadmap items
• Develop domain expertise in business metrics and data definitions