IT Data Architect (Guadalajara)

Overview

Databricks Architect to lead the design, implementation, and optimization of enterprise-grade data platforms and AI-enabled workloads for a general food corporation. This is a hands-on technical leadership role responsible for translating business and data requirements into scalable Databricks-based solutions with governance, performance, and operational resilience.

Responsibilities

• Lead the architecture, design, and implementation of Databricks-based data platforms, pipelines, and AI/ML workloads.
• Define scalable lakehouse patterns using core Databricks capabilities (Apache Spark, Delta Lake, Databricks SQL, MLflow) and governance components.
• Design robust batch and streaming data pipelines for high-volume, business-critical workloads.
• Establish architecture standards for performance optimization, workload orchestration, reliability, observability, and cost control.
• Collaborate with data engineers, analysts, data scientists, DevOps teams, and business stakeholders to deliver end-to-end solutions.
• Ensure strong data governance, security, and compliance practices across the Databricks environment.
• Support AI and advanced analytics use cases by enabling reliable feature engineering, model lifecycle practices, and production-ready data foundations.
• Demonstrate deep knowledge of distributed data processing concepts and performance tuning for complex data engineering workloads.
• Design and deliver enterprise data solutions using Delta Lake and lakehouse architecture principles.
• Leverage Python and SQL effectively; Scala experience is advantageous.
• Build and optimize ETL/ELT pipelines, data models, and large-scale ingestion frameworks.
• Experience supporting AI/ML workloads on Databricks, including MLflow, model lifecycle considerations, and production-ready data preparation.
• Apply cloud-native architecture patterns on AWS, Azure, or GCP, including storage, networking, identity, and security integration.
• Knowledge of data governance, access control, lineage, and compliance in enterprise environments.
• Familiarity with CI/CD, infrastructure-as-code, monitoring, and operational best practices for data platforms.
• Engage in deep technical problem-solving and contribute to complex delivery scenarios with minimal ramp-up time.

Qualifications

• Strong command of Apache Spark and distributed data processing concepts with ability to optimize performance for large-scale data workflows.
• Proficiency in Python and SQL; knowledge of Scala is a plus.
• Experience designing and delivering enterprise data solutions using Delta Lake and lakehouse architectures.
• Proven track record building and optimizing ETL/ELT pipelines and data models for large datasets.
• Experience supporting AI/ML workloads on Databricks (including MLflow) and production-ready data preparation.
• Solid knowledge of cloud-native architectures on AWS, Azure, or GCP, including storage, networking, identity, and security integration.
• Experience with data governance, access control, lineage, and compliance frameworks in enterprise environments.
• Familiarity with CI/CD, infrastructure-as-code, monitoring, and operational best practices for data platforms.