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Required Skills : ETL Testing focused on Big Data, python automation, and working on Google Cloud Platform (GCP) Experience with gcp platform development tools Pub/sub, cloud storage, big table, big query, data flow, data proc, and composer desired.
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Experience of implementing Amazon EMR or Big Data technologies like Hadoop, Spark, Presto, Hive will be a plus Prior domain experience of Life Insurance, Annuity or Financial Services is a plus AWS Certifications are considered a strong plus Excellent verbal and written communication skills.
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Experience in performing ETL and data engineering work by leveraging multiple google cloud components using Dataflow, Data Proc, BigQuery. Experience in utilizing GCP Services like Big Query, Composer, Dataflow, Pub-Sub, Cloud Monitoring.
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Hadoop/Big Data/Databricks Certifications. Performance tuning in Big Data environments, Spark, ADF, Snowflake. Understanding of Big Data technologies such as Spark, NiFi, Impala, Sqoop, Hive, and File formats like Parquet, AVRO, ORC, CSV, JSON.
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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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Hands-on production experience with data pipeline orchestration systems such as Airflow for creating and maintaining data pipelines. Expanding, scaling, and standardizing the core foundational principles through consistent observability, lineage, data quality, logging, and alerting across all engineering teams in the Data organization is imperative to the creation of a single pane of glass.
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Implementation shared libraries in Scala and Python that abstract complex business logic to allow consistent functionality across all data pipelines across the Data organization. Maintain detailed documentation of your work and changes to support data quality and data governance requirements.
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Contribute to maintaining, updating, and expanding existing Core Data platform data pipelines in Scala and Python / Spark while maintaining strict uptime SLAs. At least 5 years of data engineering experience developing large data pipelines.
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In doing so, they contribute to a series of cross-functional services and technologies, including Quality Assurance, Data Analytics, Software Development, Broadcast Infrastructure, and Networking & Security.
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As a member of the Core Data team you will establish the foundational set of data pipelines and datasets which are a vital key to success - enabling dozens of engineering and analytical teams to unlock the power of data to drive key business decisions and provide engineering, analytics, and operational teams the critical information necessary to scale the largest streaming service.
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The Core Data & Services for the Data organization within the DEET organization is in search of a Senior Data Engineer. Ensure high operational efficiency and quality of the Core Data platform datasets to ensure our solutions meet SLAs and project reliability and accuracy to all our stakeholders (Engineering, Data Science, Operations, and Analytics teams.
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Knowledge in Kafka, Pig, MapReduce, HBase, Sqoop, Solr is a plus Willingness to do Certification in Databricks Certified Developer: Apache Spark 3.
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Experience with GCP products such as BigTable, BigQuery, Dataproc, PubSub or other data tools like Kafka, Hive, or Apache Beam. Project categories: besides product requests and enhancements, we work on projects in performance and cost optimization, security and legal compliance, reliability and quality initiatives, as well as architectural refreshes such as POC in restructuring our data to be more streaming/real time friendly, new REST API for relevant services, migration of apps to Spark, Parquet, and other new technologies that could make our processing faster, more scalable, maintainable and cost effective.
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Fluent in SQL in big data warehouse systems such as Snowflake, BigQuery, Databricks, etc. Internal knowledge of open source or related big data technologies. As a member of the Data Engineering - Business ETL team, you own the data warehouse and data pipelines that are used for financial and regulatory reporting.
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Must be proficient in SQL and understand different data modeling techniques using Big Data frameworks such as Presto/Snowflake/Databricks. The FreeWheel Data Processing and Data Warehouse team is responsible for creating and managing the frameworks for large scale ETL using modern frameworks such as DBT and Spark.
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big data jobs Company: Allied Universal in Vienna, France
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