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Build and optimize scalable ETL pipelines, real-time data ingestion, and multi-tenant analytical databases to power sub-second BI dashboards and embedded analytics for a media-focused SaaS platform.
Build and maintain scalable data pipelines and cloud data warehouses (Snowflake/AWS Redshift/Azure Synapse) to power AI-driven healthcare analytics in Saudi Arabia, integrating HL7/FHIR clinical data.
Design and maintain scalable data pipelines and cloud data warehouses to power AI-driven healthcare solutions in Saudi Arabia, integrating clinical and claims data using HL7/FHIR standards.
Build and maintain the data pipelines and retrieval layer that power Mirai’s Generative AI products on AWS, including vector stores, embeddings, and governed datasets.
Build and maintain scalable data pipelines and cloud data warehouses (Snowflake/AWS Redshift) to power AI-driven healthcare analytics in Saudi Arabia, integrating clinical and claims data via HL7/FHIR.
Build and maintain data pipelines and lakehouse layers for generative AI products using Python, SQL, and AWS tools like S3, Glue, and Redshift.
Design and maintain scalable data pipelines and ETL processes using Spark, Kafka, and Airflow, and optimize data storage in cloud warehouses and lakes to support analytics and ML initiatives.
Lead the architecture and strategy of JLL’s enterprise data platform, consolidating global systems into a unified, scalable data layer to power AI-driven insights and analytics for commercial real estate.
Lead the build-out of a Redshift-based analytics platform to modernize reporting, reduce database load, and improve PIR workflows for a government-focused product.
Senior trainer teaching remote Data Engineering on AWS with Python and SQL, designing ETL pipelines, and mentoring students in hands-on labs.
Build and maintain ETL pipelines in Python, Snowflake, and Airflow to ingest, transform, and deliver retail analytics data for financial use cases.
Build and maintain AWS-based ETL pipelines using Python, PySpark, Glue, Lambda, and Redshift to move and transform data for analytics.
QA engineer who designs and automates tests for Databricks data pipelines and cloud migrations on AWS/Azure, validating data accuracy and ETL logic.
Designs and maintains scalable data pipelines, warehouses, and governance frameworks using Python, SQL, and cloud tools to ensure clean, reliable data for analytics and reporting.
Build and maintain ETL pipelines, dashboards, and AI agents to support renewable-energy operations, using SQL, Python, and BI tools like Tableau or PowerBI.
Design and build scalable cloud data pipelines and platforms using AWS services, Python, PySpark, and Databricks to deliver reliable, high-quality data for analytics and decision-making.
Build and optimize cloud-native data pipelines and warehouses on AWS using Python, ETL/ELT tools, and modern data stacks for analytics and ML workloads.
Lead the build-out of a Redshift-based analytics platform, migrating reporting workloads from PostgreSQL and ensuring scalable, auditable reporting for internal teams.
Builds Databricks pipelines and migrates Redshift data to Databricks using AWS services like S3, Glue, and Athena.
Build and run Databricks pipelines and jobs to migrate data from Amazon Redshift, using AWS services like S3, Glue, and Athena while applying data governance practices.
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