Senior AWS Cloud Data Engineer and Architect with over 8 years of experience designing, automating, and optimising large-scale data platforms. Specialist in AWS services (S3, Glue, Lambda, Redshift, RDS, Athena, Step Fun…
Senior AWS Cloud Data Engineer and Architect with over 8 years of experience designing, automating, and optimising large-scale data platforms. Specialist in AWS services (S3, Glue, Lambda, Redshift, RDS, Athena, Step Functions), Python, SQL, PySpark, and orchestration tools such as Apache Airflow. Proven success in building scalable ETL/ELT pipelines, enabling analytics and machine learning, and embedding governance frameworks. Adept at collaborating with cross-functional teams, conducting design reviews, and delivering secure, cost-effective solutions in hybrid environments.
Designed and implemented ETL/ELT pipelines using AWS Glue, Lambda, Step Functions, and S3.
Integrated structured and unstructured data into Redshift and RDS, enabling unified analytics.
Built CI/CD pipelines with Terraform and Jenkins, reducing deployment errors by 30%.
Conducted design reviews with architects and development leads, ensuring compliance with governance standards.
Partnered with data scientists to operationalise predictive models in Databricks using PySpark.
Delivered measurable outcomes: 40% faster queries, 20% cost reduction, 99.9% uptime.
Designed and implemented ETL/ELT pipelines using AWS Glue, Lambda, Step Functions, and S3, Integrated structured/unstructured data into Redshift and RDS, enabling unified analytics, Built CI/CD pipelines with Terraform and Jenkins, reducing deployment errors by 30%, Conducted design reviews with architects and development leads, ensuring compliance with governance standards, Partnered with data scientists to operationalize predictive models in Databricks using PySpark, Delivered measurable outcomes: 40% faster queries, 20% cost reduction, 99.9% uptime
Built ETL workflows integrating clinical and operational data using Python, SQL, and Spark.
Automated reporting pipelines, reducing manual effort by 50%.
Collaborated with stakeholders to define KPIs and improve reporting accuracy.
Applied governance and compliance standards, ensuring secure handling of sensitive data.
Built ETL workflows integrating clinical and operational data using Python, SQL, and Spark, Automated reporting pipelines, reducing manual effort by 50%, Collaborated with stakeholders to define KPIs and improve reporting accuracy, Applied governance and compliance standards, ensuring secure handling of sensitive data
Designed and maintained ETL workflows using SQL, Python, and Hadoop ecosystem tools (HDFS, Hive, Spark).
Migrated legacy systems to modern data platforms, achieving 99.9% uptime.
Automated reporting workflows, reducing turnaround time by 80%.
Partnered with IT and clinical teams to validate data quality and ensure operational alignment.
Designed and maintained ETL workflows using SQL, Python, and Hadoop ecosystem tools (HDFS, Hive, Spark), Migrated legacy systems to modern data platforms, achieving 99.9% uptime, Automated reporting workflows, reducing turnaround time by 80%, Partnered with IT and clinical teams to validate data quality and ensure operational alignment
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