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Experience deploying and maintaining pipelines (AWS, Docker, Airflow) and in engineering big-data solutions using technologies like Databricks, S3, and Spark. Experience engineering big-data solutions using technologies like EMR, S3, Spark, Databricks.
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Experience in writing spark streaming jobs (producers/consumers) using Apache Kafka or AWS Kinesis is required · Should have knowledge in variety of data platforms such as Redshift, S3, Teradata, Hbase, MySQL/PostGres, MongoDB · Experience in AWS services such as EMR, Glue, S3, Athena, DynamoDB, IAM, Lambda, Cloud watch and Data pipeline · Must have used the technologies for deploying specific solutions in the area of Big Data and Machine learning.
$70 - $75 an hourExpandApply NowActive JobUpdated 12 days ago - UpvoteDownvoteShare Job
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Experience designing and developing Big Data processing solutions using Dataflow/Apache Beam, Bigtable, BigQuery, PubSub, GCS, Composer/Airflow, and others. Strong understanding of Google Cloud Platform services and their applications in data engineering and analytics, including Big Query, Vertex AI.
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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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3+ years experience with Big Data technologies, such asApache Hadoop, Hive or Spark, Databricks and/or Snowflake. The ideal candidate will lead the architecture and implementation of on-premises and cloud big data solutions as part of enterprise data modernization.
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Advanced Analytics - Applied Econometrics, Advanced Calculus, Statistical Methods, Data Visualization, Decision Analytics & Optimization, ML, Big Data, Social Network Analytics. Techniques: Statistical Analysis, Visualization, Optimization, Machine Learning, Big Data, Data Warehouse, NLP.
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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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Plant monitoring and care during vegetative and flowering phase: trimming and staking; big leafing and pruning; plant care, watering and feeding; plant monitoring and climate control; pest and disease control; inventory data input.
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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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Expertise in various big data technologies, both open source and cloud native, AWS preferred (Kinesis, Athena, PySpark, Airflow) Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and AWS big data technologies like Redshift and Glue.
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Expertise in Cloudera Big Data Technologies, including Cloudera Distribution of Hadoop (CDP), Apache Hadoop, Apache Spark, Apache Hive, Apache Impala, and Apache HBase, with hands-on experience in deploying, configuring, and optimizing these technologies for large-scale data processing and analytics.
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These technologies may include artificial intelligence, machine learning, statistical techniques, mixed integer linear programs, reporting/big data analysis, dashboarding, data integration, and other heuristics/algorithms.
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Experience in Big Data ecosystem : ETL, tooling of Big Data Platform (Apache Spark, Airflow), Datalake, Synapse or Snowflake. As a Senior Reliability Engineer, you will play a critical role in ensuring the robustness, availability, and performance of our cutting-edge Data Engineering and Machine Learning Platforms.
$82,000 - $185,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Specifically, this experience must be in writing Big Data data engineering jobs for large scale data integration in AWS. Prior experience in writing Machine Learning data pipeline using Spark programming language is an added advantage.
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Experience with data warehousing platforms (e.g., Databricks, Amazon Redshift, Snowflake) and big data technologies (e.g., Hadoop, Spark). The Machine Learning Engineer will be responsible for architectural design and planning, advanced data pipelines, model integration and optimization, scalability, performance and research and innovation supporting production AI systems.
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big data jobs Title: developer Company: Walt Disney
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