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Design and build scalable Azure Databricks Lakehouse data platforms using Python, SQL, and Spark; collaborate with Data Science teams on modern data architectures.
Build and maintain cloud data pipelines for a major Chilean bank, integrating AWS and Azure services to ensure reliable, high-quality data for analytics and BI.
Build and maintain a modern data platform on Google Cloud Platform using Lakehouse and Medallion architecture, creating scalable pipelines and analytics-ready datasets for business teams.
Build and maintain a federated lakehouse platform for a LatAm SaaS group, transforming siloed data into trusted, auditable domains that power real-time financial decisions across Chile, Mexico and Argentina.
Senior Data Engineer builds scalable cloud data pipelines and Lakehouse architectures using Python, SQL, PySpark, and Azure for modern data projects.
Build and optimize GCP-based data pipelines and Lakehouse architecture to transform raw data into strategic business assets using BigQuery, Dataform, and Medallion model.
Lead a team to design and build scalable data platforms using Microsoft Fabric, Databricks, and Azure, enabling enterprise analytics and governance.
Design and lead the transformation of Takealot’s data platform from batch to real-time, setting technical standards and governance for a high-scale ecommerce and logistics ecosystem.
Build and maintain scalable data pipelines and lakehouse solutions on Azure/Databricks to deliver trusted datasets for analytics and AI workloads while ensuring governance, security, and compliance.
Design and govern enterprise AI architectures on Databricks, including MLOps pipelines, Unity Catalog governance, and generative AI use cases like RAG and LLM integration.
Designs and implements secure data and cloud solutions using Microsoft Fabric and Azure for client projects, focusing on scalable architectures, Lakehouse patterns, and Power BI models.
Designs and scales enterprise data solutions on Databricks, builds Spark pipelines, and productionises AI models for live/batch inferencing.
Design and implement scalable AI/ML platforms on Databricks Lakehouse, leading data engineering, MLOps, and generative AI solutions for enterprise use cases.
Lead a team to design and build scalable Azure-based data pipelines using Synapse, Fabric, and PySpark, while mentoring engineers and driving best practices in cloud data engineering.
Lead the design and build of a modern Azure-based data platform, owning scalable pipelines, governance, and mentoring engineers to power enterprise analytics and ML.
Design and build scalable data pipelines and lakehouse architectures on Databricks using PySpark/Scala and Delta Lake, ensuring production readiness with CI/CD and monitoring.
Design and deliver modern data platforms on AWS, Azure, and GCP, using cloud data warehouses, Spark, Kafka, and ML tools to build scalable analytics and AI solutions for enterprise clients.
Build and deliver Databricks-based lakehouse pipelines and cloud-native data architectures for clients, using Python/Scala/SQL.
Senior Data Engineer builds and deploys Databricks-based Lakehouse data pipelines using Spark, Delta Lake, and dbt to productionize AI models and migrate enterprise data platforms.
Lead a team to design and deploy Snowflake-based data architectures, build ETL/ELT pipelines, and productionise AI models for clients using cloud-native tools and best practices.
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