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A Senior Data Engineer designing, building, and optimizing data pipelines, models, and dashboards using AWS services (Redshift, Glue, S3, Lambda, RDS, Aurora) and producing MIS reports/BI dashboards with SAP and Power BI.
Data Engineer at CARiNG Pharmacy designing and maintaining scalable data pipelines, ETL/ELT processes, and cloud-based data infrastructure (GCP/BigQuery) to support analytics and AI initiatives.
Data Engineer designing and maintaining a central data platform (Databricks, Snowflake, Python, SQL) supporting analytics and AI/ML workloads at a precision instruments company.
The Data Engineer will design and maintain data infrastructure, including databases, data pipelines, and machine learning tools like feature stores and experiment tracking. The role focuses on optimizing data quality and efficiency within a financial services environment using Python and big data technologies.
Builds and maintains data pipelines, migrating systems to a new framework while ensuring data quality, integrity, and reliability using Python, Java, Kafka, Airflow, and cloud storage solutions.
Data engineer designing scalable ETL/ELT pipelines with Python, Shell, and SQL on Hadoop/Linux infrastructure, collaborating with stakeholders to align technical solutions with business needs.
Designs and maintains scalable data pipelines using Python, Spark, and SQL on Linux, with DevOps practices for reliable data processing.
Data engineer responsible for migrating pipelines to a GDP framework, building ingestion scripts, governing data quality, and deploying containerized apps using Docker and Kubernetes, with core technologies including Python, Airflow, Iceberg, Datahub, and Ranger.
Design and operate AWS data pipelines and ML lifecycle automation, translating data science experiments into production-grade applications using ETL, containerization, and CloudFormation.
Hybrid Senior Data Engineer building MLOps and ETL pipelines on AWS, containerizing applications, and managing cloud infrastructure (CloudFormation, SageMaker, Lambda, S3) for a large insurance company.
The AI Data Engineer will build and manage scalable data pipelines on Google Cloud Platform to support AI applications, specifically focusing on Retrieval-Augmented Generation (RAG). The role involves designing ETL/ELT processes and collaborating with data teams to extract insights from various data sources.
Data Engineer responsible for collecting, validating, processing, and analyzing data from multiple sources to build dashboards and insights, collaborating with developers, DBAs, and stakeholders to support business decisions.
The Data Engineer will design, build, and maintain scalable data pipelines, databases, and data warehouses to ensure reliable data access for analytics and business reporting. The role involves implementing ETL/ELT processes and optimizing data infrastructure using technologies like Python, SQL, Spark, and cloud platforms.
Build and maintain scalable Azure-based data pipelines and ETL processes to deliver analytics-ready models for business insights.
The Data Engineer will design, develop, and maintain robust data pipelines and ETL processes using Azure Synapse Analytics and Databricks. They will collaborate with cross-functional teams to build scalable data infrastructure and ensure data quality across the organization's data lakehouse.
The Junior Data Engineer will develop and maintain ETL/ELT pipelines, manage data lake and warehouse solutions, and ensure data quality for analytics. The role involves working with SQL, Python, Java, or Scala to support data integration and machine learning workflows.
The Cloud Data Platform Engineer will maintain, monitor, and scale cloud-based data infrastructure while implementing Infrastructure as Code. The role involves collaborating with architects to set standards and supporting data engineers by troubleshooting platform issues and automating deployment pipelines.
The Data Engineer will build and maintain ETL/ELT pipelines, manage cloud data platforms, and ensure data quality using Python, SQL, and various cloud data warehouse technologies. The role involves collaborating with cross-functional teams to support both batch and real-time data processing.
Build and maintain scalable data pipelines on Google Cloud to fuel AI systems, focusing on RAG workflows and high-quality data for machine learning models.
Designs and maintains ETL pipelines, builds dashboards, and analyzes data to support government digital transformation projects using SQL, Python/R, and BI tools.
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