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Data governance: Defining governance policies and procedures to ensure data quality, security, and compliance throughout the organization, including metadata management, data privacy, and regulatory compliance.
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Develop data migration strategies and processes, ensuring data quality and consistency across platforms, utilizing Azure Data Migration Service and Azure Database Migration Service.
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Leads engineering teams and guides members in best practices for data movement, data quality, master data management (MDM), data masking, data sub-setting and/or data virtualization development (collectively referred to as data integration and data management activities.
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Data Integrity ("DI") is responsible for data management, data governance, and data quality consumed by the functions within FRM. DI leverages tools to manage data quality based on logical rules and thresholds across one or more of the following dimensions: accuracy, completeness, conformity, consistency, coverage, duplication and timeliness.
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Familiarity with big data platforms, such as Azure Databricks, Azure HDInsight, or Azure Stream Analytics. Your proficiency in data modeling, database design, and data warehousing will serve as the bedrock for pioneering innovations, coupled with hands-on experience across various data sources and platforms, with a specific emphasis on Microsoft Azure.
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Data Warehousing, Data modeling, Data mastering, Data quality, ETL, Informatica, Alteryx, MSSQL, Oracle, Teradata, Hadoop, Snowflake, MSSQL, SSIS, SSRS, PowerBI, Tableau, Python, R, Scala, SPARK a další programovací jazyky, reporting, ETL nebo další datové nástroje.
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Experience implementing solutions using at least one of the following technologies Azure Data Factory, Azure Event Hubs, Azure Data Lake Storage, Azure Function Apps, Azure Synapse Analytics, AWS Glue, AWS S3, AWS Lambda Functions, AWS Redshift, Databricks, Snowflake, Google Big Query, Alteryx, SSIS, Informatica.
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Broad expertise and experience with distributed systems, streaming systems, and data engineering tools, such as Kubernetes, Kafka, Airflow, Dagster, etc. Deep knowledge of Python, SQL, database design, and master data strategies.
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Perform data quality checks, ensure data integrity, and resolve data-related issues. This role will primarily focus on Looker and BigQuery, and the ideal candidate will have a strong background in data engineering, SQL, and data modeling.
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The Data Science team at Cash App derives valuable insights from our extremely unique datasets and turns those insights into actions that improve the experience for our customers every day. Advanced proficiency with SQL and data visualization tools (e.g. Tableau, Looker, etc.
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Designing and developing Azure-based data solutions using Azure Data Factory, Azure Databricks, Azure SQL Database, and Azure Synapse Analytics. Databricks, Snowflake, Python and Data Modelling skills.
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Ability to design data integrations and implement data quality framework. clear and up-to-date documentation for data processes, data lineage, and system architecture.
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Data Governance and Compliance | Establish and enforce data governance policies and agreed upon best practices to ensure data quality, security, and compliance with regulatory requirements.
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Experience managing production data pipelines or warehousing data (e.g., Modern Data Warehouse stacks such as dbt, Airflow, Looker, Cloud Composer, BigQuery, Snowflake). Within People Analytics, the Business Intelligence team performs the role of Analytics Engineers, making sure people data is available, useful, trusted, and secure.
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Lead Data Scientist Engineer. Senior Data Scientist Engineer. ETL / Data Engineer - Remote/ Wrocław. Passionate about building data engineering systems, technical experience and skill set, Python development experience (not required but preferred), positive attitude and desire for skill growth.
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data quality jobs Title: data scientist Company: Deloitte in Snowflake, AZ
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