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Experience in data processing roles such as database developer/administrator, ETL/Big Data developer, data analyst and/or BI analytics developer Experience with cloud platforms, big data platforms and one or more general purpose programming languages, including but not limited to: Python, SQL, Spark, Amazon Web Services Experience with DevOps principals and version control (Git preferred) End-to-end experience with data, including querying, aggregation, analysis, and visualization.
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In-depth knowledge of big data technologies such as Hadoop, Spark, Kafka, and cloud platforms such as AWS, Azure, GCP, Snowflake, Databricks, etc. Professional certifications in cloud computing (e.g., AWS Certified Solutions Architect, Microsoft Certified Azure Solutions Architect, Azure Data Engineer, SnowPro Core) and/or big data technologies.
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Experience in Big Data tools and technologies such as Big Query, Databricks, Snowflake, Spark, Kafka Streams. In-depth understanding of Big Data ecosystems underlying architectures to re-platform legacy systems and build integration solutions on Google Cloud platform.
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Architect and drive the end-end Big Data deployment automation from vision to delivering the automation of Big Data foundational modules (Cloudera CDP), prerequisite components and Applications leveraging Ansible, Puppet, Terraform, Jenkins, Docker, Kubernetes to deliver end-end deployment automation across all ServiceNow environments.
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Utilize Spring-Boot and Flask frameworks for creating microservices that interact with Big Data systems. Cloudera Certified Professional (CCP) or any relevant Big Data certification is preferred.
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Our tenant is a strong cross-domain team to deliver E2E solutions covering tech areas ranging from machine learning, big data, microservices to data visualization. 3+ years of big data development experience with technical stacks like Spark, Flink, Singlestore, Kafka, Nifi and AWS big data technologies.
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Building automation monitoring tools for Big Data and working with data engineering-related tools and technologies, including: Kafka, Data Warehouses, and Apache Airflow.
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Experience with Azure Synapse, Big Data or Snowflake is a plus. Skills required: Azure Data Factory, DataBricks, Data Lake Architecture, Azure DevOps, Python, SQL, solid understanding of Azure fundamentals and security best practices, Release management.
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Advanced programming experience and big data experience within Python, Containerization (Docker and Kubernetes), GIT and GIT lifestyle, Databricks, Hive, Kafka, spark, PowerBI. 2+ years of experience in Big-data tools like spark, databricks.
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As a big data engineer at Booz Allen, you’ll implement data engineering activities on some of the most mission-driven projects in the industry. We need an experienced data engineer like you to help our clients find answers in their big data to impact important missions—from fraud detection to cancer research to national intelligence.
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Experience working with cloud or on-prem Big Data/MPP analytics platform (i.e. SnowFlake, Netezza, Teradata, AWS Redshift, Google BigQuery, Azure Data Warehouse, or similar). 3+ years' of experience with workflow management engines (i.e. Airflow, Luigi, Prefect, Dagster, digdag.io, Google Cloud Composer, AWS Step Functions, Azure Data Factory, UC4, Control-M.
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We are interested in a variety of topics including large-scale distributed systems, stream processing, edge computing, applied machine learning and AI, big graphs, natural language processing, big data management, and heterogenous data analytics.
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A strong foundation in statistics, mathematics, computer science, or a related field, with technical expertise in data analytics, big data analytics, automation, and data visualization tools.
$156,000 - $312,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Experience in Big Data tools and technologies such as Big Query, Databricks, Airflow, Spark, Kafka Streams. Experience in test automations ensuring data quality, continuous integrations build and deployment processes using Docker, GitHub, Jenkins and Unix/Linux shell scripts.
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Experience with big data technologies such as Apache Spark. Utilize Microsoft Azure services and tools to build and manage data solutions, including but not limited to Azure Data Factory, Azure SQL Database, Azure Databricks, and Azure Synapse Analytics.
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