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Our analytics engineers work across our full technology stack (Databricks, Spark, Airflow, GitHub, Python, Monte Carlo, etc) for developing and maintaining these datasets. Airflow, DataBricks, Git, Monte Carlo.
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Experience with big data and workflow management technologies like Hadoop, Spark, Redshift, Athena, Airflow, etc. Data platform: Hadoop, Hbase, OpenSearch, Kafka, Spark, Flink, Go, Java, SQL, Databricks, Snowflake, BigQuery.
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Have experience with both proprietary and open-source big data technologies and platforms (Snowflake, Vertica, Hive, Spark, Presto, Airflow). Have experience with both proprietary and open-source big data technologies and platforms (Snowflake, Vertica, Hive, Spark, Presto, Airflow.
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The Data Platform team is responsible for ingestion of tens of thousands of events per second, data Lakehousing (Databricks), OLTP stores (Hbase, OpenSearch) and APIs. The customers of the Data Platform are the internal business units who use it for data engineering, AI/ML, and Business Intelligence.
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We utilize proprietary and open source technologies, Kafka, Spark, Iceberg, Airflow, Presto, etc. Proficiency in SQL and multiple programming languages for building data pipelines including Python, Scala, Spark, and Java. Experience in multi-threaded, concurrent programming and synchronization Cloud technology experience on platforms like AWS, Microsoft Azure, Google Cloud Experience developing Big Data applications using java, Spark, Kafka is a huge plus.
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G Airflow, DBT, Spark, Hive, Parquet/ORC, Protobuf/Thrift, etc) g Databricks, Snowflake, Redshift, Hive, SQL/NoSQL, columnar storage formats) and methodologies (e. g Airflow, DBT, Spark, Hive, Parquet/ORC, Protobuf/Thrift, etc.
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Proficiency in designing and implementing ETL processes and data integration workflows using tools like Apache Airflow, Informatica, or Talend. Strong proficiency in programming languages such as Python, Java, or Scala, with expertise in data processing frameworks and libraries (e.g., Spark, Hadoop, SQL, etc.
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Experience using big data technologies (Snowflake, Airflow, Kubernetes, Docker, Helm, Spark, pySpark) Experience designing and building highly available, distributed systems of data extraction, ingestion, normalization and processing of large data sets in real time as well as batch, that will be used across engineering teams using orchestration frameworks like Airflow, KubeFlow or other pipeline tools.
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Python, Apache Airflow, Apache Spark, PostgreSQL, Presto SQL, Snowflake SQL. 2+ years of experience with Airflow. 2+ years of experience with Spark. Python, Apache Airflow, Apache Spark, PostgreSQL, Presto SQL, Snowflake SQL.
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And orchestration (e.g. Google Workflow, AirFlow Composer), including engagement with the open source community (and potentially making contributions to such tools) A Senior Data Engineer should be deeply familiar with the tools of modern data engineering (e.g. Spark, Kafka, Storm) and of their customers, and engaged with the open source community surrounding them – potentially, even to the level of contributing pull requests.
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Therefore, we prioritize individuals coming from the following communities: Refugee, Neurodivergent, Single Parent, Blind or Low Vision, Deaf or Hard of Hearing, Black, Hispanic, Asian, Military Veterans, the Elderly, the LGBTQ, and Justice Impacted individuals.
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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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Familiarity with AWS cloud platform and the associated big data frameworks (Spark, Redis, Bigquery/Redshift, Airflow, Glue, Snowflake, Databricks). Familiarity with AWS cloud platform and the associated big data frameworks (Spark, Redis, Bigquery/Redshift, Airflow, Glue, Snowflake, Databricks.
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You will primarily develop on / interact-with Google Cloud Platform and other technologies such as BigQuery, Airflow, Python, SQL, Spark/PySpark, Linux, Docker, Kubernetes. You will primarily develop on / interact-with Google Cloud Platform and other technologies such as BigQuery, Airflow, Python, SQL, Spark/PySpark, Linux, Docker, Kubernetes.
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Demonstrated knowledge of advanced data science toolsets (e.g., Tensorflow, Pytorch, Scikit-Learn, Airflow, Spark) Demonstrated knowledge of advanced data science toolsets (e.g., Tensorflow, Pytorch, Scikit-Learn, Airflow, Spark.
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