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Google Cloud Platform Services: DataFlow, BigQuery, Cloud Storage, DataProc, Airflow, Composer, Pub/Sub and Memorystore/Redis. Experience in performing ETL and data engineering work by leveraging multiple google cloud components using Dataflow, Data Proc, BigQuery.
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The primary focus involves the migration of data landing and transformation jobs from Hadoop to Airflow (Astronomer) and BigQuery. Experience with Airflow (Astronomer). Experience with BigQuery.
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You have worked with Dagster/Airflow, BigQuery, GCP, Terraform, Kubernetes, sklearn, keras/TensorFlow/pytorch, dbt, data modeling, Python/Pandas data frameworks, and scalable technical concepts/solutions.
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Desired experience in: Looker / Google Data Studio, BigQuery, Apache Airflow / Google Cloud Composer. Experienced with Google Cloud Composer / Apache Airflow or similar data orchestration services.
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Keywords : GCP, Google Cloud Platform, BigQuery, Composer, SQL, Python, Data Engineering, Data Pipelines. Experience working with GCP technologies like BigQuery, Composer, GCS, DataStream, Dataflows.
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Experience with streaming / data processing technologies such as Beam, Spark, Kafka, Airflow, HBase, Presto. Proven experience building enterprise-grade software in a cloud-native environment (GCP or AWS) using cloud services such as GCS/S3, Dataflow/Glue, Dataproc/EMR, Cloud Function/Lambda, BigQuery/Athena, BigTable/Dynamo.
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Experience with cloud-based data platforms and services (e.g., AWS, Azure, Google Cloud) including familiarity with relevant tools and technologies (e.g., S3, Redshift, BigQuery, etc.) Proficiency in designing and implementing ETL processes and data integration workflows using tools like Apache Airflow, Informatica, or Talend.
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Advanced experience in SQL in big data warehouse systems such as Snowflake, BigQuery, Databricks, etc. Experience with workflow orchestration management engines such as Airflow, Dagster, DBT, etc.
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Extensive experience using Snowflake, AWS SageMaker, GCP/GCP BigQuery, DBT, and Airflow for AI/ML model deployment and operationalization. This includes, but is not limited to, setting up SageMaker instances, creating Docker images, configuring lambda functions, setting up orchestration layer via Airflow, deploying endpoints, and creating API gateways.
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Familiarity with advanced statistical and machine learning techniques such as clustering, gradient boosting machines (GBM), support vector machines (SVM), neural networks (e.g., ANN, RNN, CNN), and other deep learning algorithms is a plus.
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Our infrastructure – including USDC, a blockchain-based dollar – helps businesses, institutions and developers harness these breakthroughs and capitalize on this major turning point in the evolution of money and technology.
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B. Kafka, Spark, Snowflake, GCP, BigQuery, Java, Python, SQL, Airflow und gängiger Data-Science-Libraries und -Tools. B. Kafka, Spark, Snowflake, GCP, BigQuery, Java, Python, SQL, Airflow und gängiger Data-Science-Libraries und -Tools.
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As a member of the Data Engineering Blockchain team, you own the ETL/ELT pipelines and data warehouse that are used to analyze trends and activity on the blockchain. Experience with Cloud Services (AWS, Google Cloud, Microsoft Azure, etc.
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Strong programming skills with one or more languages such as Python, PySpark, Apache Spark, Java, JavaSript, C, C. Use best practices to design and develop feature stores on Snowflake and other platforms to support rapid AI/ML model development.
$185,800 - $323,400 a yearFull-timeExpandApply NowActive JobUpdated 6 days ago - UpvoteDownvoteShare Job
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Experience in SQL and NoSQL, such as MySQL, PostgreSQL, Cassandra, HBase, Redis, DynamoDB, Neo4j, etc. Speed and efficiency are motivators for our success and our employees live by our company values: Multistakeholder, Mindfulness, Driven by Excellence and High Integrity.
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