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Is required Working knowledge of test planning, test data management, automated & manual test execution, and test management tools to support complex data and cloud landscape Working knowledge with Oracle, Teradata, SQL Server, ETL tools and data warehouse architectures highly preferred Working knowledge of QA methodologies, life cycle and processes Experience in Maintaining and Managing Test Environments for Integration and End to End testing.
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Work You'll Do As a Master QA Automation Engineer, you will help lead the development of quality control standards, processes, and corrective actions to ensure appropriate use of best practices and tools.
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Create Data Quality (DQ) checks for the data assets, monitor DQ readouts and triage issues with Devops as well as upstream source data owners. Partner with HQ systems engineering and business units to process data in stage, journal, base and semantic within Teradata and MinIO/ AWS.
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Position Summary Risk and Financial Advisory - Hybrid Operate Master QA Automation Engineer - PDM Manager Are you an experienced, passionate pioneer in technology who wants to work in a collaborative environment.
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As a Master QA Automation Engineer in our Hybrid-Operate business, you'll have the opportunity to gain valuable hands-on experience, share ideas, and collaborate with leading professionals as we engage a diverse set of industries with effective solutions Recruiting for this role ends on 04/30/24.
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Deloitte Transactions and Business Analytics LLP is not a certified public accounting firm. As used in this posting, "Deloitte Advisory" means Deloitte & Touche LLP, which provides audit and enterprise risk services; Deloitte Financial Advisory Services LLP, which provides forensic, dispute, and other consulting services; and its affiliate, Deloitte Transactions and Business Analytics LLP, which provides a wide range of advisory and analytics services.
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Strong credentials on Data Migration, Teradata, Test Data Management, etc. In the role of Technical Test Lead, you will act as an SDET Lead / Consultant and single point of contact for driving Quality Engineering directly with clients in a multi-vendor environment.
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Experienced knowledge in data analysis using SQL, user story development, data warehouse design and modeling (3rd normal form or dimensional modeling), data integration design and development and/or performance tuning Teradata SQL.
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Participate in Data Governance meeting to provide guidance and advocate how we can help the business be more successful, including participation with Data Management organizations to Define ownership, usage, security and quality for all data.
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Experienced knowledge in data analysis using SQL, user story development, data warehouse design and modeling (3rd normal form or dimensional modeling), data integration design and development and/or performance tuning Teradata SQL. Knowledgeable in data warehouse market best practices, development standards and methodologies.
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7 years of experience: performing data integration utilizing ETL methodologies, SQL, and Stored Procedures in large-scale environments; developing solutions for data warehousing including architecture, data sourcing, acquisition, integration, transformation, and presentation; and designing and developing SQL queries utilizing Teradata Stored Procedures to load and update data in reporting data marts.
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This lead senior role will collaborate and/or partner with business areas, project teams, IT technical teams, as well as outsourced vendors to ensure high quality delivery of data architecture and data integration applications focusing on business analytical enablement for medium to large project or support efforts.
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Experience with Data Quality & Master Data Management and working with data governance teams. 5+ years of experience with design and developing backend solutions involving Teradata, Oracle or Snowflake Cloud Datawarehouse in a multi terabytes environment.
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Good understanding of Data integration, Data Quality and data architecture. Experience in preparing test scripts and test cases to validate data and maintaining data quality.
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Data Engineering - Includes Data acquisition/wrangling, data ETL, data cleansing, data mining, data integration with data quality controls.
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