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Experience in Real Time Data Ingestion using GCP PubSub, Kafka, Spark or similar. Keywords: GCP, Google Cloud Platform, BigQuery, Composer, SQL, Python, Data Engineering, Data Pipelines.
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Proficiency in Databricks, Azure services (e.g., Azure Data Lake Storage, Azure Synapse Analytics), and Spark for big data processing and analytics. We are seeking a skilled and experienced Data Engineer with expertise in Databricks, Azure, and Spark to join our dynamic team.
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Minimum 3 years' experience working with Cloud technologies such as Synapse pipelines, Azure Data Factory (ADF), ADLS, Azure Databricks, Spark, Azure Synapse, and other modern data ETL/ELT technologies.
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Familiarity with the Technology stack available in the industry for data management, data ingestion, capture, processing, and curation: Kafka, StreamSets, Qlik/Attunity, GoldenGate, Hadoop, Hive, Hbase, Cassandra, Spark, Flume, Hive, Impala, etc.
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2 years of experience with prototyping and cloud bulk data ingestion techniques (e.g. AWS, Spark, Kafka) Data Storage technology (SSD, HDD, etc.) 3 years of hands-on prototyping and programming knowledge (e.g. Python, SQL/NoSQL, Kafka, Spark, etc.
$65 an hourExpandApply NowActive JobUpdated 10 days ago - UpvoteDownvoteShare Job
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Our tech stack:AWS, Snowflake, Hadoop, DataBricks, Spark, R, Python, SQLIn this roleYou will design and implement scalable, reliable distributed data processing frameworks and analytical infrastructure using multiple technologies, including data sets or data warehouses, data virtualization and services, and repositories of semi-structured data sets.
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Manage and optimize Apache Spark pools for high-performance data processing tasks. Designs, develops, and implements ETL/ELT processes using Azure Data Factory, Synapse pipelines, Databricks Notebooks, Event Hub to build integrations from Saas sources such as Service Now, Workday, Dynamics CRM, and Eloqua.
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Technical Skills:Proficiency in Databricks, Apache Spark, and AWS services (e.g., S3, Redshift, Glue, EMR, Lambda). Preferred certifications: AWS Certified Solutions Architect, Databricks Certified Data Engineer, or similar certifications.
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CF; Big Data Technologies: Proficient with big data technologies such as Databricks, Hadoop, Spark, and Kafka. %CF; 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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As a key member of our data engineering team, you will work with cutting-edge technologies such as Apache Spark, Hadoop, Kafka, and cloud services from AWS and Azure. Design and implement scalable and reliable data pipelines using big data technologies like Apache Spark, Hadoop, and Kafka.
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Preferred experience would include a background in Databricks DBT, Python, Airflow, Azure Data Factory, and/or KAFKA. Team members will work to deliver customer needs within our enterprise data warehouse, which may include data ingestion, flattening, alerting, testing, transformation, optimization, and/or data mart development.
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5+ years of experience designing, building, and supporting real-time and batch data pipeline and analytical solutions with Hadoop (Hive, Kafka, Spark, Spark Streaming, HBase) and Teradata, ETL tools like Informatica, Talend, Data Fusion, Python/Java, Oracle/Exadata.
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We are on the forefront of CBRN defense and we are looking for talented Data Scientists that have applied experience in the fields of artificial intelligence, machine learning and/or natural language processing to join our team.
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Preferred: 2+ years of experience with a modern data stack (Databricks, Kafka, Spark, Airflow, real-time databases) and cloud data warehouses such as RedShift and Snowflake. The ideal candidate for this role will have a background in building large-scale, data-intensive applications with SQL and NoSQL, high-throughput and responsive internal and external APIs for data products, and experience in user experience design.
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Technical Proficiency: Expertise in SQL and programming languages such as Python; familiarity with big data technologies like Apache Hadoop, Spark, and Kafka. Professional Experience: At least 5 years of experience in data engineering, with a strong focus on data integration and pipeline construction in cloud environments and enterprise data warehouses like Snowflake.
$215,000Full-timeExpandApply NowActive JobUpdated Today
spark data jobs Company: Intuit Inc
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