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What you will be responsible forDevelop/ Build and enhance data pipelines using Python, Spark based data engineering solutions (Databricks), and SQL (AWS Redshift). Hands on development in Python, PL/SQL, SQL, Shell Scripting, AutoSys. Hands on experience working in cloud data platforms such as AWS Redshift, Spark based data engineering solutions.
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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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O 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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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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Experience with cloud-based data platforms and services (e.g., AWS, Azure, Google Cloud) including familiarity with relevant tools and technologies (e.g., S3, Redshift, BigQuery, etc.) Strong proficiency in programming languages such as Python, Java, or Scala, with expertise in data processing frameworks and libraries (e.g., Spark, Hadoop, SQL, etc.
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6+ years of experience in SQL, data transformations, statistical analysis, and troubleshooting across more than one Database Platform (Cassandra, MySQL, Snowflake, PostgreSQL, Redshift, Azure SQL Data Warehouse, Databricks, etc.
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The tech stack is primarily Java, AWS serverless, EMR, EC2, and ECS with services written in Scala, Spark, and Python, and uses Snowflake as our Database. 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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3+ years of experience in SQL, data transformations, statistical analysis, and troubleshooting across more than one Database Platform (Cassandra, MySQL, Snowflake, PostgreSQL, Redshift, Azure SQL Data Warehouse, Databricks, etc.
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Experience in optimizing data models and access patterns for modern warehouse and orchestration platforms such as Snowflake, Redshift, Databricks, Spark, DBT, Apache Airflow, and Prefect.
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Your skills include experience with QlikView, Adobe Analytics, Google 360, Python, R Programming, Scala, Apache Spark, and AWS Redshift. Your skills include experience with QlikView, Adobe Analytics, Google 360, Python, R Programming, Scala, Apache Spark, and AWS Redshift.
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Experience working with big data technologies and ETLs like Spark, S3, and Redshift. Knowledge of Python, Scala, Spark, Athena, or Databricks. Knowledge of Python, Scala, Spark, Athena, or Databricks.
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Experience with distributed data and computing tools such as Spark, Databricks, Hadoop, Hive, AWS EMR, or Kafka. Experience with data warehousing such as AWS Redshift, MySQL, or Snowflake.
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2+ years of data warehousing experience (Redshift , Snowflake , Databricks, etc.) Implementing ETL processes, moving data between systems including S3, Snowflake, Kafka, and Spark. 3+ years experience with distributed data/computing tools (MapReduce, Hadoop, Hive, EMR, Kafka, Spark, Gurobi , or MySQL.
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Our stack spans AWS and GCP, with technologies like Airflow, Redshift, BigQuery, Postgres, Spark, and dbt. Our stack spans AWS and GCP, with technologies like Airflow, Redshift, BigQuery, Postgres, Spark, and dbt.
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Familiarity with cloud data storage solutions such as AWS S3, Google Cloud Storage, Azure Blob Storage, AWS Redshift, Google BigQuery, Azure SQL Data Warehouse, or others. Experience with cloud data processing and analytics services such as AWS Glue, Google Dataflow, Apache Spark on cloud platforms, or Azure Data Lake Analytics.
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