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Preferred Qualifications: Working knowledge of Distributed Parallel Processing Environments such as Spark or Snowflake Working knowledge of problem solving/root cause analysis on Production workloads Working knowledge of Agile, Scrum, and Kanban Working knowledge of workflow orchestration using tools such as Airflow, Prefect, Dagster, or similar tooling Working knowledge with CI/CD and automation tools like Jenkins or Azure DevOps Strong verbal and written skills in English language.
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Designing and implementing performant data ingestion pipelines from multiple sources using Databricks on Azure. Databricks / Spark. 2+ years of work experience in a highly technical and hands-on data engineering, or data science role.
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We are hiring an experienced Big Data Engineer with a strong focus on GCP to join our team in Phoenix, AZ. The ideal candidate will have extensive experience in Hadoop, Spark (Batch/Streaming), Hive, and Shell scripting and solid programming skills in Java or Scala.
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Supports the extraction of data using a variety of a programming languages, such as SQL, SAS, Python, or Spark/Scala, and applies knowledge of relational and non-relational database systems housed on both on-premise and off-premise (hybrid) platforms.
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Architect cloud infrastructure solutions like Kubernetes, Kubeflow, OpenStack, Ceph, and Spark either On-Premises or in Public Cloud (AWS, Azure, Google Cloud) If you have a passion for the latest open source technologies, cloud-native development, data center technologies such as Kubernetes and computer networking, you will love the Alliances Engineer role at Canonical.
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No Minimum years of experience: 8-10 years Certifications Needed :No Job Details: Must Have Skills Indepth Big Data, Hadoop, Hive Queries in AWS Groovy Scripting, testing, test automation, debugging REST API testing in AWS, Spark Scala jobs testing Nice to have skills AWS Databricks Delta Lake, Delta Live tables Detailed Job Description 6-8 years of IT experience focusing on Testing and Automation experience using Java and Groovy.
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Experience leading and architecting enterprise-wide initiatives specifically system integration, data migration, transformation, data warehouse build, data mart build, and data lakes implementation / support.
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Demonstrating relevant project management experience in organizing and leading teams including Big data engineering (Hadoop, Spark, Scala, Kafka) and ETL pipeline development (tools: IICS/AWS Glue/SAP BODS/ SSIS/SnapLogic); preferable in P&C Insurance data warehouse.
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Hands-on experience with Spark or ScalaGood experience in end-to-end implementation of data warehouse and data martsPreferred Qualifications: Strong understanding of databases and big data software technologies, specifically Azure and Databricks.
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Experience leading and architecting enterprise-wide initiatives specifically in automation testing for ETL testing and several phases of testing, data migration, transformation. We provide strategic and technical advice, and we have expertise in areas covering Artificial Intelligence, Cloud Migration, Custom Software Development, Data Analytics Infrastructure & Cloud Solutions, Cyber Security Services, etc.
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Applying knowledge and relevant work experience in Big data engineering (Hadoop, Spark, Scala, Kafka) and ETL pipeline development tools (tools: IICS/AWS Glue/Matillion/Abinitio SSIS/SnapLogic); preferable in P&C/L&A Insurance data warehouse.
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Experience with data frameworks such as Spark, Kafka, Kubernetes and Airflow. Experience with data frameworks such as Spark, Kafka, Kubernetes and Airflow. The Trust and Safety Experience team owns all customer facing components of the Trust & Safety Platform and supports the user interfaces for Affirm Products like Card, Savings, Prequal, Checkout, User Portal and Repayment flows across various geographies.
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Experience with Big Data frameworks; Spark, Hive, Hadoop, Flink, Operational Data Stores (ODS), amongst other foundational data warehousing and modeling principles. Experience with Python, Google Cloud, Big Query, Machine Learning and Data Automation / Visualization techniques.
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Must have strong hands-on experience on data processing tasks using Spark/Python for cleansing and curating to populate analytical models. Strong Data transformation/ETL skills using Spark, Python, PySpark, HiveQL.
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Big Data such as Hadoop (MapR), Spark, Yarn. Job Title: Big Data Engineer (4 position ) This individual's day-to-day work will involve using Java, Scala, SQL, Big Data, Linux, git, and maven/gradle to design and build software solutions that are fast, scalable, and flexible.
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