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Our tech stack includes (but is not limited to) languages and technologies like Golang, Python, SQL, shell scripts, AWS EC2, Athena, Aurora / PostgreSQL, Kafka / MSK, Kubernetes, SQLite, Airflow, Spark, and more.
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You will learn and contribute to API (Java, python), data processing (Spark, EMR), new partnership data onboarding (DynamoDB), AWS serverless technologies (lambda) and supporting existing production processes.
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Strong programming skills with one or more languages such as Python, PySpark, Apache Spark, Java, JavaSript, C, C. We’re Salesforce, the Customer Company, inspiring the future of business with AI+ Data +CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way.
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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 Architect role at Canonical.
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Advanced Knowledge of Big Data Technologies: Candidates with experience in distributed systems, such as Apache Hadoop, Apache Spark, or Apache Flink or cloud datawarehouses like. 7+ years of experience as ML engineer/data-scientist with hands-on experience with machine learning.
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Building data ingestion pipelines is highly technical and requires advanced skills with computers and proficiency with Big Data tools and technologies such as SQL, Python/Scala, Spark, Spark Structured Streaming, Spark SQL, Kafka, Sqoop, Hive, Kudu, HBASE, Impala, S3, HDFS, or Cloud platforms such as AWS, GCP, Azure, etc.
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Certified Analytics Professional (CAP), Data Science Council of America (DASCA), Principal Data Scientist (PDS), SAS Big Data Professional, Microsoft Certified Azure Data Scientist Associate, or equivalents.
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Experience with big data technologies such as Databricks, Spark, SparkR. Metas Solutions has an "immediate" opening for an experienced Data Scientist/Machine Learning Engineer to work Remotely.
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Analyzing large data sets using programming languages such as Python, R, SQL and/or Spark; Extracting and aggregating data from large data sets using SQL/Hive or Spark.
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Skills Required: Requires experience in the following: Agile SDLC; Application Architecture Disciplines; Jenkins; NodeJS; Spring; Java; Python; React; Shell Scripting; SQL; REST; Maven; Apache Ant; JSON; Apache Zookeeper; AWS Cloud Services; Oracle; Apache Spark; Splunk; GIT; Junit; Unit Testing; and User Acceptance Testing.
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Proficiency in data engineering tools and technologies - SQL, Python, Spark, DBT, Airflow, Kafka. Expertise in architecting, designing, and building end to end data platforms in the GCP environment using BigQuery and other services while adhering to best practices guidelines such as open standards, cost, performance, time to market and minimize vendor lock.
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Because we know that heat arises the intersection of complementary forces, our professionals come from myriad disciplines and backgrounds: data, analytics, and insights, content and creative production, communications and strategy, finance and marketing, and sociology, psychology, and other liberal arts disciplines.
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Deep experience with data pipelines (ETL/ELT, Spark, dbt, Airflow, Databricks, etc.) Verusen is a leading technology company that uses artificial intelligence to provide visibility, digitization and prediction of materials data and inventory for complex supply chains.
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Knowledge of Big Data platforms (e.g. Hadoop, Map/Reduce, Spark, HBase, HDInsight, Data Bricks, Hive) and with programming languages like UNIX shell scripting, Python etc. In addition, they will also assist in developing the data pipeline (using Informatica BDM or Python) to ensure ongoing data collection, consolidation, and management.
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Experience in one or more of the following languages: Python, SQL, Spark. Expertise in Statistical Programming Software such as R, and experience in data analysis. This role provides an opportunity to develop models for deployment on the Quantitative Finance Analytical Platform, a proprietary platform for advanced computational needs within Corporate Treasury.
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