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Design and implement AWS-based data and AI platforms for APAC clients, ensuring scalable, compliant solutions that drive business outcomes.
Build and maintain AWS-native data pipelines and warehouses for telecom analytics using S3, Redshift, Glue, and Python/SQL.
Design and build scalable, cloud-native data pipelines and data-lake architectures on AWS using S3, Glue, Redshift, and Kinesis.
Lead the build-out of a Redshift-based analytics platform, migrating PostgreSQL workloads and optimizing performance for fast, auditable, and scalable reporting.
Design and build scalable AWS data pipelines and real-time streaming systems using Confluent Kafka for analytics and AI workloads.
Designs and builds scalable data infrastructure and ETL pipelines using Python, Java, SQL, and cloud tools to support analytics and ML.
Designs and maintains scalable cloud data pipelines and warehouses, enabling analytics and ML with tools like Spark, Airflow, and Snowflake.
Builds and maintains scalable data pipelines and infrastructure using Python, SQL, and cloud services to support analytics and machine learning workflows.
Lead the design and optimization of scalable Azure data pipelines using Databricks, PySpark, and ADF, while mentoring engineers and collaborating with cross-functional teams.
Designs and maintains scalable data pipelines and cloud-based data solutions to support analytics, reporting, and machine learning initiatives using Python, SQL, and cloud platforms like AWS/Azure/GCP.
Build and maintain cloud-native AWS data platforms, ETL/ELT pipelines, and data lakes to support analytics and BI use cases.
Lead a team that builds and maintains scalable data pipelines and warehouses for a Philippine bank, using Python, SQL, Snowflake, DBT, and Airflow to ensure clean, reliable data for analytics and decision-making.
Design and build scalable data platforms on AWS, Azure, and GCP, using cloud warehouses, Spark, Kafka, and Python/Java to enable AI-driven analytics for enterprise clients.
Designs and builds scalable cloud data pipelines, warehouses, and analytics platforms using Python, SQL, Spark, and cloud tools like BigQuery and Snowflake, with optional Gen AI integration.
Lead a team building and maintaining scalable data pipelines for analytics using ETL/ELT tools, Airflow, DBT, and Snowflake/Redshift.
Lead the end-to-end data architecture—ingestion, processing, modeling, and consumption—using Python, PySpark, Airflow, and AWS services to enable reliable reporting and LLM-driven analytics at scale.
Build and maintain scalable data pipelines and cloud infrastructure (GCP/AWS/Azure) to process large datasets, using SQL and Python for ETL and data quality monitoring.
Designs and maintains scalable data pipelines and warehouses, transforming raw data into insights for decision-making using Python, SQL, and cloud platforms.
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