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5+ years in designing, solutioning the Databricks, Snowflake, PySpark and Big data tools including DataOps, Data engineering, Data Integration, Data Orchestration and Data Marketplace.
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Design, develop, and implement data architecture solutions using SQL Server, Microsoft Azure, Snowflake, and Databricks. The ideal candidate will have extensive experience in designing and implementing data solutions, with proficiency in SQL Server, Microsoft Azure, Snowflake, Databricks, and other relevant technologies.
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Must Have skills: MDM, Experience in writing queries (SQL, Python, R, Scala) as needed and experience with various data technologies such as Azure Synapse or SQL Server, Data Modeling, Snowflake, Databricks.
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Required 10 Years Hands-on experience with Azure services such as Azure Data Factory, Azure Databricks, Azure Data Lake Storage, Azure SQL, Azure Synapse Required 5 Years Hands-on experience in SQL Server (SSIS, SSRS, SSAS), ORACLE, T-SQL Required 10 Years Hands-on experience in Azure SQL Database, Azure SQL Datawarehouse Required 7 Years Hands-on experience in deploying and maintaining large-scale data processing pipelines.
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Your expertise in ETL, DataBricks, Python, Spark, Scala, JavaScript/JSON, SQL, and Jupiter Notebooks will be essential in ensuring efficient data processing and analysis. Job Responsibilities:- Design, develop, and implement end-to-end data pipelines, utilizing ETL processes and technologies such as DataBricks, Python, Spark, Scala, JavaScript/JSON, SQL, and Jupyter Notebooks.
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Develop, deploy and maintain data processing pipelines using cloud technology such as AWS, Kubernetes, Airflow, Redshift, Databricks, EMR. Experience with data warehouse platforms such as Redshift, Databricks, Big Query, Snowflake.
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Expert in applying SCD types on S3 data lake using Databricks/Delta Lake. AWS/Kafka/Databricks or similar certifications. Expert knowledge in writing complex pySpark, SQL, dbt and ETL development with experience processing extremely large datasets.
Full-timeExpandApply NowActive JobUpdated 3 days ago - UpvoteDownvoteShare Job
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Experience with data streaming such as Kafka. Plan, design, build, test and deploy data warehouse and data mart solutions. Experience with data quality processes, data quality checks, validations, data quality metrics definition and measurement.
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At least 4 years of experience in data engineering working with Big Data Technologies: Apache Spark, Pyspark, Hadoop, and DataBricks with delta lake. Experience in ETL, SQL, Informatica, Power exchange, Databases like SQL server, DB2, Oracle.
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You will have indepth knowledge and experience in; cloud data lake house; Databricks (preferred) or Snowflake; cloud-native solutions knowledge; SQL or Python; modern DevOps approach; have written data code AND also the CI/CD pipelines.
$140,000 - $165,000Full-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Design, develop, and implement end-to-end data pipelines, utilizing ETL processes and technologies such as Databricks, Python, Spark, Scala, JavaScript/JSON, SQL, and Jupyter Notebooks.
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Implement Ingestion of new batch and streaming data pipelines using Scala, Databricks, and Airflow. The Core Data & Services for the Data organization within the DEET organization is in search of a Senior Data Engineer.
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IBM Datastage, Erwin, SQL Server (SSIS, SSRS, SSAS), ORACLE, T-SQL, Azure SQL Database, Azure SQL Datawarehouse. Experience with key data warehousing architectures including Kimball and Inmon, and has a broad experience designing solutions using a broad set of data stores (e.g., HDFS, Azure Data Lake Store, Azure Blob Storage, Azure SQL Data Warehouse, Azure Cosmos DB.
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Hands-on experience building data pipelines using Hadoop components Sqoop, Hive, Solr, MR, Impala, Spark, Spark SQL., HBase. 4-5 experience building data pipelines using Hadoop components Sqoop, Hive, Solr, MR, Impala, Spark, Spark SQL., HBase.
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Advanced data analytics designing and building solutions using technologies such as Azure Data Factory, Azure Data Lake, HD Insights, SQL DWH, Stream Analytics, Machine Learning, R Server, Synapse, Azure Machine Learning (AML), Azure Kubernetes Service (AKS) for model deployments, Vector and Retrieval-Augmented Generation (RAG) implementation.
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