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Build and optimize distributed data pipelines using Scala/Java/Python, Spark, Kafka, and Delta Lake/Iceberg, then deploy them on GKE with Helm for AI-driven networking analytics.
Build and maintain scalable data pipelines and curated datasets on a modern Lakehouse and AI data platform, using Python, SQL, Spark and Kafka to support analytics and AI use cases.
Lead a team of data engineers to build and scale a modern Data Lakehouse (AWS S3, Iceberg, EMR) and transformation frameworks (dbt) for a global streaming platform, enabling self-serve analytics and AI readiness.
Design and maintain scalable data pipelines, ETL/ELT processes, and cloud-based data warehouses using Spark, SQL, and cloud platforms like AWS/Azure/GCP.
Design and build scalable cloud-native data platforms for a major bank, using Lakehouse architectures, real-time streaming, and AWS to deliver analytics-ready datasets.
Build and optimize distributed data pipelines and AI analytics for networking using Spark, Kafka, Delta Lake, and Kubernetes in a cloud-native environment.
Build and maintain cloud-native data pipelines and lakehouse systems using Spark, PySpark, Iceberg, and streaming tools to modernize OCBC’s financial data infrastructure.
Build and maintain Goldman Sachs’ Lakehouse and AI data platform, designing scalable pipelines, curated datasets, and data-quality controls to power analytics and AI use cases in a fast-paced finance environment.
Principal Software Engineer at Cloudera leading the design of large-scale distributed systems using Java, Spark, Kafka, and Iceberg, while mentoring teams and solving complex data-platform challenges.
Build and enhance a multi-dimensional indexing engine for Data Lakehouses using Java, Scala, and Rust, supporting open table formats like Iceberg.
Build the real-time data backbone for a new adtech standard that turns physical retail shopper behavior into anonymized, measurable audiences using edge computing, streaming pipelines, and privacy-preserving AI.
Build the data backbone for a new global Retail Media channel: transform raw sensor streams from thousands of stores into real-time measurement, forecasting, and attribution systems using Spark, Kafka, Trino, and privacy-preserving pipelines.
Build and maintain cloud or on-premise data pipelines and advise clients on modern data solutions using Python, SQL, Spark, and cloud platforms.
Design and build an AI-ready Data Lakehouse in Saudi Arabia using Delta Lake/Iceberg, Spark, and Kafka, ensuring compliance with NCA/NDMO 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 cloud-native data pipelines on AWS, landing data into Snowflake via dbt Cloud and Python, while optimizing performance and cost for analytics-ready datasets.
Build and operate a high-throughput crypto market-data platform: ingest exchange APIs, run streaming/batch pipelines, and maintain storage backends like Iceberg, ClickHouse, and PostgreSQL to serve trading desks and analytics.
Build and maintain systematic option data platforms for a hedge fund, collaborating with quants to deliver real-time pricing and reference data for trading decisions.
Designs and builds scalable data pipelines and warehouses using Python, Spark, and Snowflake to support analytics and ML workloads for enterprise clients.
Designs and builds scalable data architectures using Apache Iceberg, NebulaGraph, Spark, and Airflow to ensure traceable, governed, and accessible data pipelines.
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