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Our team leverages cutting-edge open-source technologies such as Apache Spark, Flink, Kafka, Airflow, Delta Lake, and Iceberg to build and maintain a scalable, high-quality data ingestion framework.
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Develop, deploy and maintain data processing pipelines using cloud technology such as AWS, Kubernetes, Airflow, Redshift, Databricks, EMR. Expert in applying SCD types on S3 data lake using Databricks/Delta Lake.
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Analyze data to ensure it meets specific data management, data governance, and data quality assurance requirements before processing the data within the Data Lake and data warehouse.
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Bonus if those were open-sourced big data processing frameworks using technologies like Spark, Airflow, Kafka, Flink, Iceberg, Deltalake. Cutting-Edge Technology: Work with advanced open-source technologies such as Apache Spark, Flink, Kafka, Airflow, Delta Lake, and Iceberg.
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By integrating this data with our online systems, we empower multiple business lines, drive critical machine learning models, and fuel fast-paced experimentation. Prior technical experience in Big Data solutions - you've built meaningful pieces of data infrastructure.
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Data Ingestion to one or more Azure Services - (Azure Data Lake, Azure Storage, Azure SQL, Azure DW) and processing the data in Azure Databricks. Hands on experience working with Azure Cloud services like Azure Data Factory, Azure Data Lake Storage, Azure Even hub, Azure SQL, Azure Analytical services, Azure Cosmos DB.
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Streamlining data processing experience including formalizing concepts of how to handle lake data, defining windows, and how window definitions impact data freshness.
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The Core Data & Services for the Data organization within the DEET organization is in search of a Senior Data Engineer. Tech stack includes Airflow, Spark, Databricks, Delta Lake, Snowflake, Scala, Python.
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Implement Ingestion of new batch and streaming data pipelines using Scala, Databricks, and Airflow. Experience with at least one major Massively Parallel Processing (MPP) or cloud database technology (Snowflake, Redshift, Big Query.
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Hands-on production experience with data pipeline orchestration systems such as Airflow for creating and maintaining data pipelines. Expanding, scaling, and standardizing the core foundational principles through consistent observability, lineage, data quality, logging, and alerting across all engineering teams in the Data organization is imperative to the creation of a single pane of glass.
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Implementation shared libraries in Scala and Python that abstract complex business logic to allow consistent functionality across all data pipelines across the Data organization. Maintain detailed documentation of your work and changes to support data quality and data governance requirements.
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Contribute to maintaining, updating, and expanding existing Core Data platform data pipelines in Scala and Python / Spark while maintaining strict uptime SLAs. At least 5 years of data engineering experience developing large data pipelines.
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In doing so, they contribute to a series of cross-functional services and technologies, including Quality Assurance, Data Analytics, Software Development, Broadcast Infrastructure, and Networking & Security.
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Job Title: Data Engineer/ Data ArchitectLocation: Corvallis, OR – 5 Days Onsite Role Job Description: At least 8 to 10+ years’ experience in data engineering , AI and ML and Aws/Azure Architect, Design and implement distributed data processing pipelines using Spark, Python, SQL and other tools and languages prevalent in the Big Data/Lakehouse ecosystem.
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Biotrack (Seed to Sale) data entry verification and adjustments for Processing Department products. NAV data entry and adjustments for Processing Department products. This position is responsible for compliantly organizing starting materials in METRC, NAV and Google Sheets for the Extractions, Kitchen & Processing Packaging Teams.
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lake data processing jobs Title: data engineer Company: Everest
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