76 to 100 of 200 PySpark Jobs

efficient deployments and robust system performance. Working within Azure, you'll integrate advanced analytics platforms like Databricks and build data pipelines using Python and PySpark to support large-scale processing. The position also involves optimising data models, delivering insights through Power BI, and managing relational data with SQL. Collaboration ...

manage Ontology Objects, Links, and Actions to support downstream applications and workflows. Pipeline Development & Automation: Build scalable, fault-tolerant ETL/ELT pipelines using PySpark, SQL, and Foundry's proprietary tools like Pipeline Builder and Code Workbook. Productization & Scaling: Create Prototypes and Convert them into production-grade solutions ...

manage Ontology Objects, Links, and Actions to support downstream applications and workflows. Pipeline Development & Automation: Build scalable, fault-tolerant ETL/ELT pipelines using PySpark, SQL, and Foundry's proprietary tools like Pipeline Builder and Code Workbook. Productization & Scaling: Create Prototypes and Convert them into production-grade solutions ...

manage Ontology Objects, Links, and Actions to support downstream applications and workflows. Pipeline Development & Automation: Build scalable, fault-tolerant ETL/ELT pipelines using PySpark, SQL, and Foundry's proprietary tools like Pipeline Builder and Code Workbook. Productization & Scaling: Create Prototypes and Convert them into production-grade solutions ...

manage Ontology Objects, Links, and Actions to support downstream applications and workflows. Pipeline Development & Automation: Build scalable, fault-tolerant ETL/ELT pipelines using PySpark, SQL, and Foundry's proprietary tools like Pipeline Builder and Code Workbook. Productization & Scaling: Create Prototypes and Convert them into production-grade solutions ...

manage Ontology Objects, Links, and Actions to support downstream applications and workflows. Pipeline Development & Automation: Build scalable, fault-tolerant ETL/ELT pipelines using PySpark, SQL, and Foundry's proprietary tools like Pipeline Builder and Code Workbook. Productization & Scaling: Create Prototypes and Convert them into production-grade solutions ...

manage Ontology Objects, Links, and Actions to support downstream applications and workflows. Pipeline Development & Automation: Build scalable, fault-tolerant ETL/ELT pipelines using PySpark, SQL, and Foundry's proprietary tools like Pipeline Builder and Code Workbook. Productization & Scaling: Create Prototypes and Convert them into production-grade solutions ...

manage Ontology Objects, Links, and Actions to support downstream applications and workflows. Pipeline Development & Automation: Build scalable, fault-tolerant ETL/ELT pipelines using PySpark, SQL, and Foundry's proprietary tools like Pipeline Builder and Code Workbook. Productization & Scaling: Create Prototypes and Convert them into production-grade solutions ...

manage Ontology Objects, Links, and Actions to support downstream applications and workflows. Pipeline Development & Automation: Build scalable, fault-tolerant ETL/ELT pipelines using PySpark, SQL, and Foundry's proprietary tools like Pipeline Builder and Code Workbook. Productization & Scaling: Create Prototypes and Convert them into production-grade solutions ...

manage Ontology Objects, Links, and Actions to support downstream applications and workflows. Pipeline Development & Automation: Build scalable, fault-tolerant ETL/ELT pipelines using PySpark, SQL, and Foundry's proprietary tools like Pipeline Builder and Code Workbook. Productization & Scaling: Create Prototypes and Convert them into production-grade solutions ...

manage Ontology Objects, Links, and Actions to support downstream applications and workflows. Pipeline Development & Automation: Build scalable, fault-tolerant ETL/ELT pipelines using PySpark, SQL, and Foundry's proprietary tools like Pipeline Builder and Code Workbook. Productization & Scaling: Create Prototypes and Convert them into production-grade solutions ...

manage Ontology Objects, Links, and Actions to support downstream applications and workflows. Pipeline Development & Automation: Build scalable, fault-tolerant ETL/ELT pipelines using PySpark, SQL, and Foundry's proprietary tools like Pipeline Builder and Code Workbook. Productization & Scaling: Create Prototypes and Convert them into production-grade solutions ...

manage Ontology Objects, Links, and Actions to support downstream applications and workflows. Pipeline Development & Automation: Build scalable, fault-tolerant ETL/ELT pipelines using PySpark, SQL, and Foundry's proprietary tools like Pipeline Builder and Code Workbook. Productization & Scaling: Create Prototypes and Convert them into production-grade solutions ...

manage Ontology Objects, Links, and Actions to support downstream applications and workflows. Pipeline Development & Automation: Build scalable, fault-tolerant ETL/ELT pipelines using PySpark, SQL, and Foundry's proprietary tools like Pipeline Builder and Code Workbook. Productization & Scaling: Create Prototypes and Convert them into production-grade solutions ...

manage Ontology Objects, Links, and Actions to support downstream applications and workflows. Pipeline Development & Automation: Build scalable, fault-tolerant ETL/ELT pipelines using PySpark, SQL, and Foundry's proprietary tools like Pipeline Builder and Code Workbook. Productization & Scaling: Create Prototypes and Convert them into production-grade solutions ...

manage Ontology Objects, Links, and Actions to support downstream applications and workflows. Pipeline Development & Automation: Build scalable, fault-tolerant ETL/ELT pipelines using PySpark, SQL, and Foundry's proprietary tools like Pipeline Builder and Code Workbook. Productization & Scaling: Create Prototypes and Convert them into production-grade solutions ...

manage Ontology Objects, Links, and Actions to support downstream applications and workflows. Pipeline Development & Automation: Build scalable, fault-tolerant ETL/ELT pipelines using PySpark, SQL, and Foundry's proprietary tools like Pipeline Builder and Code Workbook. Productization & Scaling: Create Prototypes and Convert them into production-grade solutions ...

/AS programming), Python (preferable) Competitive Landscape: Knowledge of key Data & Analytics platforms such as AWS, GCP, Snowflake, etc. Experience in shipping PySpark enabled data pipelines. Understanding batch, streaming ETL/ELT processing. Preferred experience/understanding on data lake house, data mesh architectural principles. This position will ...

scalability, and cost efficiency. Design and maintain high-quality data models to support diverse analytical, ML, and GenAI workloads. Optimize SQL, Python, Spark/PySpark, and transformation workflows for performance at scale. Maintain and evolve data integrations across internal and external systems. Contribute to platform engineering, infrastructure automation … + Terragrunt Desired Qualifications: 7+ years as a Data Engineer, working with large-scale, production-grade data systems. Expert in SQL, Python, Spark/PySpark, and modern data warehousing techniques. Strong AWS experience, including serverless and scalable architectures. Proven experience designing performant, cost-efficient data pipelines (batch + streaming ...

Experience Extensive experience with Azure services including Azure Databricks, Azure Data Lake Storage, and Azure Data Factory. Advanced proficiency in SQL, Python, and Spark (PySpark), with a strong focus on performance optimization and distributed processing. Proven experience in CI/CD practices using industry-standard tools (e.g., GitHub Actions ...

build skills and knowledge relevant to the role. Essential Skills Experience of using a unified engine for large-scale data analytics (e.g. Spark/PySpark) Experience in writing, testing and implementing scripts (e.g. Python, Scala) Experience of cloud data stack use (e.g. SageMaker Notebooks, S3, Glue, Athena) Communicating technical ...

build skills and knowledge relevant to the role. Essential Skills Experience of using a unified engine for large-scale data analytics (e.g. Spark/PySpark) Experience in writing, testing and implementing scripts (e.g. Python, Scala) Experience of cloud data stack use (e.g. SageMaker Notebooks, S3, Glue, Athena) Communicating technical ...

build skills and knowledge relevant to the role. Essential Skills Experience of using a unified engine for large-scale data analytics (e.g. Spark/PySpark) Experience in writing, testing and implementing scripts (e.g. Python, Scala) Experience of cloud data stack use (e.g. SageMaker Notebooks, S3, Glue, Athena) Communicating technical ...

build skills and knowledge relevant to the role. Essential Skills Experience of using a unified engine for large-scale data analytics (e.g. Spark/PySpark) Experience in writing, testing and implementing scripts (e.g. Python, Scala) Experience of cloud data stack use (e.g. SageMaker Notebooks, S3, Glue, Athena) Communicating technical ...

Continuously improve data systems and maintain awareness of best practice and emerging approaches Essential Skills Experience with large-scale analytics engines (e.g. Spark/PySpark) and scripting languages such as Python or Scala. Hands-on use of cloud data stacks (e.g. SageMaker Notebooks, S3, Glue, Athena) and modern storage ...