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You’ll ensure the reliability of Guidewire’s large-scale data platform on AWS, managing Kafka, Spark, and Kubernetes to support AI and analytics workloads while improving automation and incident response.
Build and deploy production-grade AI and GenAI solutions, integrating models into real-world systems using Python, TensorFlow/PyTorch, and cloud-native tools.
Build and deploy production-grade AI and Generative AI systems in Python, integrating LLMs and vector databases with cloud-native pipelines on AWS.
Design and build cloud-based data pipelines and ETL/ELT processes using Python, PySpark, AWS/Azure services to support insurance analytics and modern data architecture.
Build and maintain automated test frameworks for AWS data services using Python, pytest, and boto3 to validate functionality and performance of cloud platforms like S3, Redshift, and Glue.
Build scalable ELT pipelines and self-serve dashboards to turn raw data into trusted, decision-ready insights for Product, Growth, Revenue and Operations teams.
Builds reusable data models and metrics to turn raw data into trusted insights for product, growth, revenue, and operations teams.
Lead a team building and running Cover Genius’s cloud data platform (BigQuery, Airflow, dbt, Dataflow) to ingest, process, and serve insurance-protection data for global e-commerce partners.
Build and maintain automated test frameworks for AWS data services using Python, pytest, and boto3 to validate functionality and performance of cloud platforms.
Lead a team to build and maintain scalable data pipelines using SQL and Python, ensuring reliability and collaboration across analytics, data science, and product teams.
Design and build scalable data pipelines and analytics infrastructure using Python, Spark, and AWS for large-scale data processing and real-time streaming.
Build and maintain scalable data pipelines on AWS and Databricks, using Python, PySpark, and modern ETL/ELT frameworks to deliver reliable data across the organization.
Designs and builds cloud data platforms on AWS, Azure, and GCP using SQL, Python, and ETL best practices for enterprise clients.
Designs and optimizes AWS-based data pipelines using Python, PySpark, Airflow, and Redshift/S3/DynamoDB/Snowflake for batch and real-time analytics.
Designs and builds real-time data pipelines on AWS using PySpark, Airflow, and Redshift to power enterprise-scale analytics and insights for an HR-focused SaaS platform.
Build and optimize scalable data pipelines using Python, PySpark, and Databricks for batch and streaming workloads, managing Delta Lake layers and AWS S3 storage.
Build and maintain AWS-based data pipelines and analytics platforms using services like Glue, Redshift, and SageMaker to enable data-driven insights and reporting.
Senior Data Engineer builds and maintains scalable ETL/ELT pipelines, data warehouses, and AI/ML platforms to create reusable datasets and enable customer-centric analytics and AI solutions.
Builds and optimizes data pipelines and warehouses using AWS services, Teradata, and PySpark for a telecom-focused enterprise platform.
Data Engineer | Sydney, NSW We are looking for an experienced Data Engineer (9–14 Years) to join a high-performing team delivering enterprise-scale data platforms and real-time data solutions on AWS. Must Have Skills…
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