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8+ Experience programming using data analysis, data modeling, programming, and visualization tools such as Python, SQL, Power BI, Alteryx, and Tableau. 10+ Experience programming using data analysis, data modeling, programming, and visualization tools such as Python, SQL, Power BI, Alteryx, and Tableau.
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Use programming languages including but not limited to Python, R, SQL, Java or Scala, SQL. Programming Languages: Python, R, SQL, Java or Scala, SQL. As a Machine Learning Engineer, you will partner with Data Scientists, Data Engineers, Data Analysts and other professionals.
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Python, MWAA (Airflow), Spark Optimization, AWS Glue, AWS EC2, Apache Spark. · Develop test maintain and document data pipelines using DBT Spark AWS Glue and Airflow. The ideal candidate will contribute to our Asset & Wealth Management domain leveraging their expertise in Python Apache Spark and various AWS services to drive innovation and excellence.
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Programming Language: Python, R, SQL, Java, Scala, Pyspark/Apache Spark, Shell scripting. Use programming languages including but not limited to Python, R, SQL, Java, Scala, Pyspark/Apache Spark, Shell scripting.
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Utilize Spark, Databricks, Tensorflow, and Python to handle data extraction, analysis, and model development. Lead Data Scientist - Remote. Proficiency in Python development, Tensorflow, and Databricks.
$150,000 - $170,000 a yearFull-timeRemoteExpandApply NowActive JobUpdated 1 month ago - UpvoteDownvoteShare Job
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Build complex data engineering pipelines using Python and Airflow to hydrate DataMart in Snowflake and Data Lake in AWS Cloud. · Build complex data engineering pipelines using Python and Airflow to hydrate DataMart in Snowflake and Data Lake in AWS Cloud.
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Experience building robust large-scale data pipelines and storage solutions using Python, Pyspark, Databricks, Google/Azure clouds, WCNP, Apache Airflow, and other relevant tools. Kubernetes, Python, SQL, Pyspark, GCP, Azure, and Kubernetes.
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Sr. Technology Risk Data Engineer - Financial Services (Python/SQL) Using tools such as Python, collaborate with the Risk Organization to enhance their models such as VaR. scenario analysis, stress test, risk metrics, income simulation, collateral and credit risk.
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In our Data science track we prepare you to get job as one of the following: Python developer, a data analyst, data visualization developer, a statistician, a machine learning engineer or a data scientist.
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Hands-on experience with Postgres SQL, RedShift, Redshift spectrum, AWS Glue, Athena, Snowflake utilities, Snow SQL, Snow Pipe, AWS Lambda model techniques using Python. Experience in Data Migration from RDBMS to Redshift/Snowflake cloud data warehouse.
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Strong backend development skills using languages such as Python, Node.js, or Java, and frameworks such as Flask, Django, Express. Build scalable backend services and APIs using languages such as Python, Node.js, or Java, and frameworks such as Flask, Django, Express.
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BigData, Scala, Hadoop, AWS EMR, AWS EC2, AWS Airflow, Snowflake, AWS Athena, Spark, Python, PL/SQL, Tableau, Angular, CI/CD. You should be comfortable working in a fast-paced environment and can help build APIs, Calculators, on new cutting edge cloud and big data technologies such as AWS EMR, EC2, Scala Spark, Scala, Snowflake.
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AWS Glue, Lambda, Athena, Pyspark, Redshift, EMR, AWS RDB, AWS DynamoDB, AWS Glacier, Python. Should have a deep understanding of AWS Services (Redshift, Glue, Lambda, Athena, S3, EC2), Visual Studio, SQL Server, SSIS, Matillion; Python.
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General programming experience (SQL, Python, Excel VBA, etc.) Proven ability to digest and explain complex ideas to a diverse group of stakeholders; including data visualization/dashboards using tools such as Tableau and Datorama.
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Create and maintain documentation as required (e.g. data models, data lineage, metadata, data dictionary, information supply chain) Experience - Minimum 5 years of experience with proven working experience as a data engineer or data analyst.
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