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).