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Your expertise in ETL, Databricks, Python, Spark, Scala, JavaScript/JSON, SQL, and Jupyter Notebooks will be essential in ensuring efficient data processing and analysis. Design, develop, and implement end-to-end data pipelines, utilizing ETL processes and technologies such as Databricks, Python, Spark, Scala, JavaScript/JSON, SQL, and Jupyter Notebooks.
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Work with a team with respect to discovery, design and delivery of data models, data governance models, data migration plans, data warehouse design and reporting components of applications using GCP, Databricks and Microsoft tools.
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Prior experience supporting Data Governance initiatives desired: Data Quality, Metadata Management (Data Cataloging, Data Lineage), Master Data Management, Data Security.
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Expertise in SQL Server Management, Azure Data Factory Development, Databricks Engineering, Delta Parquet File Management, and SQL Server Integration Services. Implement data cataloging, classification, and lineage using Azure Purview, developing data governance policies, and ensuring compliance.
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May recommend data analytics strategies including enterprise data warehousing, master data management, data quality, data governance, Big Data, business intelligence, visualization.
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In depth knowledge of modern data technologies including Snowflake, RedShift, Azure SQL/Synaspe, Databricks or similar technologies. Conduct data management maturity assessments and identify pain points for including data quality, governance, architecture, analytics, metadata management, master data management.
$120,000 - $210,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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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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You likely have experience with a variety of architectures, patterns, and methodologies focused on Analytics Strategy, Data Governance & Master Data Management, Data Architecture(s), Distributed Systems, ETL, Big Data, IoT, Data Visualization and Analytics.
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Must have: Proficient in SQL and database technologies (e.g., Oracle, SQL Server, PostgreSQL) with hands-on experience in ETL processes and data integration tools. Nice to have: Mentor junior developers and team members on best practices in data governance, workflow optimization, and agile methodologies.
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Data platform architecture, system design, and platform engineering of Azure cloud infrastructure, ADLS Gen2, ADF, Event Hub, Kafka, Databricks, SQL Server and Snowflake. automate export/import metadata to and from the Data Governance platform.
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Incorporate core data management competencies including data governance, data security and data quality. Cloud based tools/solutions including AWS, S3, Databricks, Snowflake, SQL, Python.
$105 - $173Full-timeExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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Strong people management skills, experience in data analysis and visualization tools, proficiency in data modeling and manipulation, knowledge of statistical analysis techniques, understanding of data governance principles, business acumen, confident communication and presentation skills, ability to work in a fast-paced environment People Management Snowflake.
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Our data environment is currently under development, and we are in the process of migrating fully to Azure Cloud, utilizing SQL Server, Boomi Integrations, Azure Synapse, PowerBI, and other monitoring and governance tools.
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As a CSA, you will partner with customers throughout the customer journey to understand what drives value, beginning from the pre-sales running proof of concepts to demonstrate quick time to value, to post-sales onboarding and implementation where you set customers up for long-term success with scalable implementation and data governance best practices.
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Experience with Google Big Query, AWS databases, Oracle SQL, mySQL, PostgreSQL, and other data technologies. Focus on the data management and ensure data governance best practices are implemented to manage data domain, data integration, data quality, and data access.
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