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As the Data Quality Manager, you will be responsible for developing and implementing procedures to identify, monitor, and resolve data quality issues across the data ecosystem.
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Implement a data quality initiative to ensure quality and accuracy of regulatory reporting data. The successful candidate will be responsible for implementing a data quality initiative to ensure the quality and accuracy of regulatory reporting data.
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Proficiency in GD&T, Blueprint Reading, Quality Inspection, Quality Control Data Analysis, Injection Molding, and AS9100. Seeking an experienced Quality Manager (also known as Quality Assurance Manager, Quality Control Manager) with a background in Plastic Injection Molding.
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Develop high-quality data engineering solutions focusing on cloud-first principles, encapsulation, repeatability, automation, and auditability. Proficiency in developing data quality and validation routines.
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Maintains accuracy in abstraction by following published guidelines and abstraction specifications; maintain accuracy and integrity of audit information by following standards and guidelines; facilitates and participates in quality assurance audits of abstracted/audited data to ensure a high level of inter-rater reliability among the clinical data abstractor team and achievement of acceptable validation results from external audits.
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Responsible for data quality and verification, data delivery, change management processes used for data extract corrections, and modification and enforcement of data standards and policies for data exchanges to FSSA and OMPP as defined by the State.
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Develop, optimize, and manage data pipelines for ETL processes using Databricks, with a focus on data integrity and quality. Establish a robust data governance strategy, defining standards, metadata management, lineage, and quality practices.
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Experience with proteomics and/or metabolomics data generated through mass spectrometry. In general, as a computational biologist at insitro, you will be in charge of performing analysis of large scale datasets with an emphasis on multimodal measurements and data integration.
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Conduct data management maturity assessments and identify pain points for including data quality, governance, architecture, analytics, metadata management, master data management.
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Data Management and Quality: data mapping, data profiling, data catalog, relational data modeling, master data management, data governance, metadata management (ex.
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Familiarity with the technology stack available in the industry for metadata management: Data Governance, Data Quality, MDM, Lineage, Data Catalog etc. Extensive hands-on experience implementing data migration and data processing using AWS services: VPC/SG, EC2, S3, Autoscaling, CloudFormation, LakeFormation, DMS, Kinesis, Kafka, Nifi, CDC processing Redshift, Snowflake, RDS, Aurora, Neptune, DynamoDB, Cloudtrail, CloudWatch, Docker, Lambda, Spark, Glue, Sage Maker, AI/Client, API GW, etc.
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In depth knowledge of modern data technologies including Snowflake, RedShift, Azure SQL/Synaspe, Databricks or similar technologies. Extensive experience or knowledge of data visualization technologies such as Tableau, Power BI, Qlik and Spotfire.
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Extensive experience or knowledge of cloud-based data platform technologies such as AWS, Microsoft Azure or Google Cloud Computing. Experience with data modeling, data prep and machine learning tools like Alteryx, RapidMinder, RStudio and Tableau Prep.
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Must have experience in architecting and implementing data architecture, data engineering, reporting and analytical solutions across multiple business functions and domains. Experience deploying enterprise-wide reporting solutions while leveraging data visualization best practices.
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Data Strategy and Governance. Should also be able to Identify the implications for data governance that arise from the technology, current processes, and skill levels in a complex data environment.
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data quality jobs Title: data specialist Company: Officeteam
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