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Hands-on experience in Scala/Big Data technologies (Spark/Flink, Hive, Oozie, Hbase/Cassandra/MongoDB, Redis, YARN, Kafka) Experience with Java/Scala programming language Experience with shell scripting Experience with ETL job design using Nifi/Streamsets/Talend/Pentaho/etc.
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Experience with large scale enterprise applications using big data open-source solutions such as Spark, Kafka, Elastic Search / Solr and Hadoop, HBase- Experience or knowledge of basic programming and DB's technologies (SQL, Python, Cassandra, PostgreSQL, AWS Aurora, AWS RDS , MongoDB, Redis, Couchbase, Oracle, MySQL, Teradata.
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Balancing the nimble spirit of a startup with the powerhouse soul of Publicis Media, Spark Foundry delivers the best of both worlds to a client roster that spans some of the world’s best and most beloved brands and companies.
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Data: PostgreSQL, Firestore, BigQuery, Elastic Search, Prefect, Kafka, Scala, Spark, dbt. Familiarity with modern ETL tools such as Apache Beam, Kafka, Spark, or similar. Experience with Scala and Spark or PySpark.
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Knowledge of Apache Spark, KAFKA, HIVE, Hbase, Flink, Airflow. Certification in any of the following preferred: CISSP, CISM, CISA, Security +, or equivalent Additional security industry training such as SANS or Offensive Security preferred Knowledge of information security concepts and technologies, including cyber risk, third party risk, and security governance Knowledge of a wide range of security/risk management frameworks like NIST, CIS etc.
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Proficient in spark, sparkSQL and hiveSQLExperience working with Snowflake / Databricks or similar distributed compute systemsExcellent understanding of scheduling and workflow frameworks. As a data engineer, you have experience spanning traditional DW and ETL architectures and big data ecosystems like Databricks / Snowflake / EMR / Airflow.
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Knowledge of information security concepts and technologies, including cyber risk, third party risk, and security governance. Knowledge of Palo Alto Nex-Gen firewall, MS PowerShell, and cloud solutions.
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Bachelor’s degree in computer science, Engineering, IT, Management Information Systems, or a related discipline Experience in Python and SQL Experience in ingesting data from a variety of structures including relational databases, Hadoop/Spark, cloud data sources, XML, JSON Experience in ETL concerning metadata management and data validation.
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Experience in architecting data pipelines and solutions for both streaming and batch integrations using tools/frameworks like Glue ETL, Lambda, Google Cloud DataFlow, Azure Data Factory, Spark, Spark Streaming, etc.
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Spark Foundry is a global media agency that exists to bring HEAT - Higher Engagement, Affinity, and Transactions - to brands. With a bottom-up culture that celebrates diversity and aims for all voices to be heard, Spark has become a magnet for the industry's best talent, with one of the best retention rates in the industry.
ExpandApply NowActive JobUpdated 12 days ago - UpvoteDownvoteShare Job
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3+ years experience with Distributed data/computing tools (MapReduce, Hadoop, Hive, EMR, Kafka, Spark, Gurobi, or MySQL) Senior Data Engineer - Principal Associate (Spark, Scala) Utilize programming languages like Java, Scala, Python and Open Source RDBMS and NoSQL databases and Cloud based data warehousing services such as Redshift and Snowflake.
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Experience with large scale enterprise applications using big data open-source solutions such as Spark, Kafka, Elastic Search / Solr and Hadoop, HBase. Experience or knowledge of basic programming and DB's technologies (SQL, Python, Cassandra, PostgreSQL, AWS Aurora, AWS RDS , MongoDB, Redis, Couchbase, Oracle, MySQL, Teradata.
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Production experience with: HDFS, YARN, Hive, Spark, Kafka, Oozie / Airflow, Amazon Web Services (AWS), Docker / Kubernetes, Snowflake. Production experience with: HDFS, YARN, Hive, Spark, Kafka, Oozie / Airflow.
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Familiarity with Agile software engineering practices Intellectual curiosity for the world of quantitative research and attention to detailsPreferred Skills:Experience with Python/SQL/R/SAS/C#Experience with distributed data processing ecosystems (Spark, DASK etc.
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Apache Spark or PySpark a plus. Workflow scheduling tools such as Apache Airflow, windows scheduler, or Luigi. Strong Python data skills with Pandas, as well as XML/JSON parsing. Working closely with Data Science and Analytics professionals, you will develop automated, streaming data pipelines for event capture, transformation, and feature extraction to assist the machine learning process.
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spark job in Chicago, IL
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