Senior Application Data Architect - GP, Remote: Colombia - Costa Rica, Fulltime.

- This position is open to candidates located in Colombia or Costa Rica only -

Application Data Architect 

This hybrid role combines technical architecture leadership (50%) and hands-on data engineering (50%). You will lead the design and evolution of enterprise data platforms while actively contributing to delivery.

You’ll collaborate with Enterprise Architecture, Data Architecture, Product Owners, and cross-functional engineering teams to translate strategy into scalable, production-ready solutions.

This role expands beyond execution, driving technical direction, standards, and system-level thinking across multiple initiatives.

Key Responsibilities

1. Architecture & Technical Leadership

• Design and evolve enterprise data architectures, including Lakehouse, Data Warehouse, pipelines, semantic models, and reporting layers.
• Define and maintain architectural standards, patterns, and best practices across Microsoft Fabric and Azure services.
• Translate enterprise data strategy into epics, features, and actionable technical stories.
• Lead technical planning activities, identifying dependencies, risks, and trade-offs early in the lifecycle.
• Establish and enforce standards for data quality, taxonomy, pipeline design, and semantic modeling.
• Review solution designs and critical implementations to ensure scalability, performance, and maintainability.
• Act as a technical leader and mentor, elevating engineering practices and system-level thinking.
• Serve as a technical integrator across Data Engineers, Data Scientists, and Architects
  

2. Data Engineering & Delivery

• Design, build, and enhance data pipelines, dataflows, notebooks, and semantic models.
• Contribute hands-on to complex and high-impact initiatives where architecture and implementation intersect.
• Support platform modernization, including migration from on-prem SQL Server to Microsoft Fabric.
• Optimize data solutions for performance, reliability, and cost efficiency.
• Collaborate with Data Scientists to productionize ML models and integrate them into enterprise pipelines.
• Apply AI-assisted techniques to improve development workflows and solution quality.
• Deliver scalable, maintainable, and high-quality data solutions aligned with best practices.

3. Operational Excellence & Collaboration

• Participate in cross-project planning and release activities.
• Collaborate with Product Owners and stakeholders to align solutions with business needs and priorities.
• Monitor systems using logs and dashboards to ensure performance, reliability, and issue resolution.
• Create and maintain clear, concise technical documentation (architecture, systems, processes).
• Contribute to a collaborative, inclusive, and team-first engineering culture.
  

Requirements

Technical & Data Engineering Expertise

• 4+ years of experience in software/data engineering (Python, PySpark, Spark or similar).
• Strong experience designing and building enterprise data platforms (Lakehouse, Data Warehouse, Analytics).
• SQL, relational databases, and large-scale data systems
• Data pipelines, ETL/ELT processes, and query optimization
• Semantic modeling and reporting tools (e.g., Power BI)
• Experience with cloud data platforms (preferably Azure / Microsoft Fabric or similar).
• Familiarity with distributed data technologies (e.g., Spark, Kafka, Hadoop or cloud-native equivalents).
• Understanding of CI/CD, DataOps/MLOps, and modern deployment practices.
• Experience working with APIs and system integrations.

Architecture & Delivery

• Proven ability to translate architectural strategy into scalable, production-ready solutions.
• Experience partnering with Enterprise and Data Architects to deliver aligned solutions.
• Strong understanding of data modeling, taxonomy, and data quality practices.
• Ability to balance hands-on development with technical leadership responsibilities.
• Experience contributing to technical design, planning, and estimation in Agile environments.

AI / ML (Preferred)

• Experience with ML frameworks (e.g., scikit-learn, TensorFlow, Azure ML) is a plus.
• Exposure to integrating ML models into production data pipelines.
• Interest or experience in applying AI/automation to improve engineering workflows and solution quality.

Professional Skills

• Strong communication skills with the ability to explain complex concepts to technical and non-technical stakeholders.
• Experience working in Agile/Scrum teams with Product Owners and cross-functional roles.
• Self-motivated, proactive, and comfortable operating with ambiguity and ownership.
• Strong attention to code quality, testing, and maintainability.
• Collaborative mindset with a focus on team success and knowledge sharing.
• Bachelor’s degree in Computer Science or related field (or equivalent experience).