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The Senior Data Engineer will build and maintain scalable data pipelines and Lakehouse architectures using Databricks, Spark, and Python. This role involves collaborating with cross-functional teams to deliver data products while driving engineering best practices and platform optimization.
The Senior Machine Learning and Artificial Intelligence Scientist will lead the development and production deployment of advanced AI and ML solutions, including generative AI and multi-agent systems, to drive business impact. The role involves working across cloud-native architectures like Azure, Databricks, AWS, and GCP to build scalable, reliable, and governed AI products.
The Manufacturing Cybersecurity Data Engineer builds and maintains ETL pipelines to consolidate OT security and asset data into the Databricks Lakehouse platform. This role involves using Python and SQL to transform data for vulnerability reporting and risk management within GM's manufacturing environments.
Designs and leads GenAI and Agentic AI solutions across multi-cloud environments, focusing on RAG, AI agents, and end-to-end architecture for data, integration, and security. Works with AWS, Azure, and GCP to build scalable, production-grade AI systems with governance and LLMOps practices.
Data Architect managing and optimizing data storage, pipelines, and ETL processes using AWS services (S3, RDS, Redshift, DynamoDB, Glue, Lambda), Databricks with Delta Lake/Spark, and Informatica IDMC for data governance and integration.
Technical Program Manager on the Data Engineering team driving roadmap, risk, and cross-cutting programs for the firm's data curation capabilities (data modelling, APIs, SQL) and Lakehouse cloud data store, partnering closely with engineers and architects.
Design, build and optimise a Databricks-based data platform using Spark, Delta Lake, Python and SQL, with IaC tools in Azure, enabling analytics and ML across a retail technology company.
Early-career Data Engineer building scientific data products and scalable data pipelines using Databricks, SQL, Python, and AWS to support AI/ML and analytics across pharmaceutical product development in Hyderabad.
The Senior CE Data Engineer builds and maintains secure, scalable data pipelines on Databricks within a healthcare-regulated environment. The role focuses on implementing medallion architectures, data contracts, and PHI isolation controls to support AI-ready data products.
The Associate Director of CE Data Engineering leads a team in building and operating a PHI-compliant data platform on Databricks for pharmacy services. The role focuses on setting engineering standards, implementing CI/CD pipelines, and ensuring data quality and security within a regulated healthcare environment.
Design, develop, deploy, and maintain AI/ML models on AWS GovCloud using SageMaker, Databricks, PySpark, and Delta Lake for federal government programs, ensuring compliance with NIST AI RMF, EO 14110, and FedRAMP standards.
Join ProMach and shine. Whether you're creative, strategic, persuasive, or mechanically inclined, there’s a place for you here. Be a problem-solver, a closer, a futurist - whatever drives you. At ProMach,…
Our team members are the key to our company’s success, and their health and well-being, as well as that of their families, is very important to us. We offer a comprehensive benefits package that allows our team members…
Senior Data Engineering Manager leading multiple teams at McKesson, overseeing Azure Data Platform (Data Factory, Synapse, ADLS), Databricks Lakehouse, and distributed data processing with Python, Scala, SQL, and Spark.
Azure Data Engineer on a 6+ month contract designing, building, and optimizing ETL/ELT data pipelines and analytics solutions using Databricks, dbt, Apache Spark, Python, Azure Data Factory, SQL Server, and Power BI.
About Amperity—Why This, Why Now Most customer data is a lie by omission. Every consumer brand wants to know its customers. Unfortunately, almost none actually do. Data is scattered across a dozen systems, half of it…
Design and build scalable ETL/ELT pipelines using Databricks, PySpark, and Delta Lake, with Python and SQL, on Azure or other cloud platforms.
Build distributed compute systems for enterprise-scale data and AI workloads, optimizing Spark, Trino, Presto, and Flink environments for cost and performance at petabyte/exabyte scale.
Builds and optimizes core lakehouse systems for AI, focusing on metadata, transactions, table maintenance, and storage efficiency at petabyte/exabyte scale using Iceberg, Delta Lake, and cloud object stores.
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