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5+ years of experience in wide variety of distributed systems and frameworks such as Apache Spark, Kafka, Airflow, TensorFlow , Docker, Snowflake and more. 5+ years of experience in wide variety of distributed systems and frameworks such as Apache Spark, Kafka, Airflow, TensorFlow , Docker, Snowflake and more.
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Experienced in using Big Data stack (Hadoop, Hive, Spark, Kafka, Airflow/OOZIE, BigQuery/Presto/Impala, etc.) Experienced in using Big Data stack (Hadoop, Hive, Spark, Kafka, Airflow/OOZIE, BigQuery/Presto/Impala, etc.
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Experience working with Airflow as workflow management tools and build operators to connect, extract and ingest data as needed. Experience in Real Time Data Ingestion using GCP PubSub, Kafka, Spark or similar.
$130,000 - $150,000 a yearFull-timeExpandApply NowActive JobUpdated 4 days ago - UpvoteDownvoteShare Job
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4+ years experience with Distributed data/computing tools (Glue, Airflow, EMR, ECS Fargate, Kafka, Spark, Aurora, or MySQL) 4+ years experience with Distributed data/computing tools (Glue, Airflow, EMR, ECS Fargate, Kafka, Spark, Aurora, or MySQL.
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Utilize programming languages like Scala, Python and Open Source RDBMS and NoSQL databases and Cloud based data warehousing services such as Redshift and Snowflake. At least 1 year experience with cloud computing (AWS, Microsoft Azure, Google Cloud.
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Expert knowledge and experience in Databricks, Lake House, Structured Streaming, Kafka, Delta Lake, Delta Live table, Delta shareetc. Explore and build proof of concepts using open source NOSQL technologies such as HBase, DynamoDB, Cassandra and Distributed Stream Processing frameworks like Apache Spark, Kafka stream.
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Data Integration experience in tools like Amazon API Gateway, S3, SQS, Airflow, Apache Kafka, Lambda, Snowflake, etc. Hands-on experience implementing cloud-based data infrastructure and integration patterns using tools like Amazon API Gateway, S3, SQS, Airflow, Apache Kafka, Lambda, Snowflake, etc.
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Good understanding of data pipeline design concepts, Hands-on experience with Postgres and Snowflake, Excellent coding skills in Python, Experience with Apache Airflow and Apache Spark tools, Knowledge of AWS, Experience with CI/CD tools and processes, Good communication skills.
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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. Capture, store and socialize data lineage and operational metadata.
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Tech proficiency in Python, Snowflake, Datamart, ML libraries, Spark, Kafka, Postgres, Cassandra, Airflow, AWS (EC2, S3, RDS, etc.) Tech proficiency in Python, Snowflake, Datamart, ML libraries, Spark, Kafka, Postgres, Cassandra, Airflow, AWS (EC2, S3, RDS, etc.
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You have orchestrated and managed large scale data platforms such as Airflow, Kafka, or Jenkins. Our workforce management platform helps some of the fastest-growing, most innovative companies in the world—including Stripe, Etsy, and Robinhood—to schedule, forecast, and organize their support teams.
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Observability and AIOPS: DataDog, Grafana, Prometheus, ELK, Elastic, Kibana, Kafka, CloudWatch, Jaeger, Zipkin, Kinesis, Apache Airflow, AppDynamics. Observability and AIOPS: DataDog, Grafana, Prometheus, ELK, Elastic, Kibana, Kafka, CloudWatch, Jaeger, Zipkin, Kinesis, Apache Airflow, AppDynamics.
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We are open to proficiency in a variety of technologies: SQL, Bash, Python, Java, Presto, Spark, AWS, DataOps, DevSecOps, data streaming like Kafka, RabbitMQ, data stacks like Airflow, Databricks, dbt, and data stores like Cassandra, Aurora, ZooKeeper.
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Familiarity with the following technologies: Hadoop, Kafka, Airflow, Hive, Presto, Athena, S3, Aurora, EMR, Spark. Experience utilizing or leading implementations leveraging ETL tools (Informatica / Talend), BI Reporting tools such as MicroStrategy, Microsoft Power BI, data modeling tools such as Erwin, Oracle, SQL server, NoSQL, JDBC, UNIX shell scripting, PERL and JAVA, XML/JSON files, SAS , Python, AWS cloud-native technologies, S3, Athena, Redshift.
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The VP, Engineering will l ead scaling the global data and software engineering team s in managing the foundational enterprise data platform and creat ing tools and frameworks that enhance data collection and processing, analytics and artificial intelligence , data dissemination , and governance.
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