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Builds scalable data pipelines and backend services using Java, SQL, and Scala to power analytics and reporting for a fintech company.
Build and maintain scalable data pipelines on Databricks using PySpark, refactor legacy ETL to modern ELT, and ensure data quality and reliability for analytics.
Design and maintain scalable data pipelines and warehouses to power analytics and ML, using SQL, Python/Java, and tools like Spark and Presto.
Design and maintain scalable data pipelines and platforms using Databricks, PySpark, and cloud ecosystems for enterprise clients across multiple industries.
Lead the design and delivery of scalable data platforms and analytics solutions using Databricks, Snowflake, and Tableau to enable self-service BI and advanced analytics across Finance, Supply Chain, and other domains.
Build and optimize data pipelines for semiconductor manufacturing using AWS, PySpark, Redshift, and Python to support analytics and AI initiatives across global fabs.
Build and maintain Azure Databricks pipelines and PySpark/SQL workloads on Delta Lake to integrate financial and ESG data for reporting and analytics.
Build and optimize Snowflake-based data platforms for clients, designing ELT/ETL pipelines and data models while collaborating with cross-functional teams.
Build and maintain secure data pipelines and models for financial clients using Databricks or Snowflake, ensuring reliable, production-ready data solutions in regulated environments.
Build and maintain scalable data pipelines on Databricks for AI/ML workloads, using orchestration tools like Airflow and dimensional modeling.
Design and maintain scalable data pipelines and ETL processes using Spark, Kafka, and Airflow, and optimize data storage in cloud warehouses and lakes to support analytics and ML initiatives.
Data Transformation & Automation Engineer Position Summary As a GSC Data Transformation & Automation Engineer , you will play a critical role in enabling data-driven decision making across Global Supply Chain…
Lead the design of scalable, AI-powered data pipelines and lakehouse architectures for a global food manufacturer, optimizing cloud costs and enforcing governance across real-time streaming and ETL workflows.
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 maintain AWS-based data pipelines and warehouses using Python, SQL, and services like Glue, Redshift, and S3 to enable analytics and ML feature stores.
Build and optimize Snowflake-based data pipelines using Matillion and Python to power analytics and AI use cases for a large retailer.
Designs and builds AWS-based data pipelines and feature stores using Python, SQL, and tools like Glue and Sagemaker to support analytics and ML workloads.
Build and maintain scalable data pipelines on Databricks and Azure, optimizing ETL/ELT processes to support analytics and ML initiatives for an insurance-focused team.
Build and maintain Azure-based data pipelines and warehouses using T-SQL, Synapse, Data Factory, and Databricks to deliver clean, reliable data for analytics and modeling.
Design and maintain enterprise data pipelines and warehouses using SQL, Google BigQuery, and IBM Infosphere Datastage to support analytics and reporting for a client.
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