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Design and maintain data pipelines, lakes, and warehouses to deliver high-quality data for analytics and AI use cases, collaborating with product teams and stakeholders.
Builds and maintains data pipelines, ETL processes, and real-time data systems using Python, SQL, and cloud tools like GCP and Cloudera Hadoop.
Designs, builds, and maintains data pipelines and analytics solutions using AWS, SQL, and big data tools to process structured and unstructured data.
Designs and builds data pipelines and analytics infrastructure using AWS services, SQL, Python, and big-data tools to support predictive modeling and business insights.
Build and migrate data pipelines from Hadoop to a modern lakehouse using Spark 3 and Iceberg, ensuring reliable reporting and analytics for core business needs.
Design and build production pipelines to migrate a legacy Hadoop warehouse to a modern lakehouse using Spark, Iceberg, and Airflow for core analytics.
Build and migrate data pipelines from legacy Hadoop to a modern lakehouse using Spark, Iceberg, and Airflow to power core analytics and reporting.
Build and migrate data pipelines for a large-scale lakehouse platform, moving legacy Hadoop workloads to Spark 3/Iceberg and optimizing complex SQL queries.
Build and maintain scalable data pipelines and warehouses using SQL, Python, and cloud tools (AWS/GCP/Azure) to feed analytics and ML workloads.
Build and migrate data pipelines to a modern lakehouse using Spark and Iceberg, replacing a legacy Hadoop warehouse while maintaining core reporting systems.
Designs and maintains batch and real-time data pipelines using Spark, Kafka, and cloud platforms like Snowflake or AWS to power BI, ML, and AI initiatives.
Senior Data Engineer at DiDi troubleshoots large-scale data pipelines, optimizes BI/ETL systems, and resolves complex data issues across global teams using SQL, Python, and cloud data warehouses.
Build and automate cloud infrastructure for an AI-powered SaaS platform, focusing on reliability, security, and scalable deployments while improving developer workflows.
Build and maintain large-scale on-premise big data platforms (500+ nodes, 23+ PB storage) for a global payments unicorn, focusing on OLAP and data engineering tooling.
Build and maintain BI pipelines that transform raw data into Power BI dashboards for Enexis’s energy-transition projects using Snowflake, Matillion, and DataVault modeling.
Build and maintain Python-based data pipelines and services using Kafka, Hadoop, Docker, and Kubernetes, ensuring scalable ingestion, processing, and secure storage for production systems.
Build and maintain Python algorithms for energy-sector optimization, using Kafka, Hadoop, and CI/CD pipelines in an agile startup environment.
Build and maintain high-performance Spark/Scala pipelines to migrate petabytes of data from legacy systems to modern cloud storage, ensuring data quality and ACID compliance.
Design and build cloud-native and on-premise data pipelines using Spark, Kafka, and cloud platforms to ingest, process, and analyze large datasets for enterprise clients.
Maintain and evolve large-scale Hadoop clusters (Cloudera) for enterprise clients, automate operations with Ansible, and support Spark/Hive/Kafka pipelines in banking/insurance projects.
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