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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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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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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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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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Ownership of data quality in our datasets and data pipelines. Experience with SQL, Python, Data Warehousing, DBT, Airflow and Kafka. You have experience with Data Warehousing, Airflow, AWS, Kafka and dbt.
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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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Experience in handling data - data lineage, data governance, ensuring data quality, feature stores etc. You will use programming languages such as Python, R, or SQL to automate the ETL, analytics, and data quality processes from the ground up.
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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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Bachelor's or Master's degree in Computer Science or related field, at least 5 years of experience in data architecture, strong knowledge of AWS, Data Vault, Snowflake, Airflow, expertise in data modeling and quality assessment, proficiency in Git and CI/CD methodologies.
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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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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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data quality jobs Title: data specialist Company: Pwc in Snowflake, AZ
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