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Expert Microsoft t-SQL and DAX development skillsExtensive experience using various data modeling techniques (Kimball, Inmon, or Data Vault)Experience using the Power Platform, particularly Power Bi and Power AutomateExperience in a multicloud environment, with a focus on GCP.
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Exp designing and delivering data engineering solutions with Databricks Exp in Azure Databricks Experience in Data Engineer in zure Big Data Environment Expertise in ETL tools i.e. (Infomatica, Talend, SSIS, Data Stage) Expertise in Implementing Data Warehousing Solutions Exp with working in Agile (KANBAN, Scrumban) environment Exp in zure stack ( zure Data Lake, zure Data Factory) Exp in Scala or Python, T-SQL.
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What you will be responsible forDevelop/ Build and enhance data pipelines using Python, Spark based data engineering solutions (Databricks), and SQL (AWS Redshift). Hands on development in Python, PL/SQL, SQL, Shell Scripting, AutoSys. Hands on experience working in cloud data platforms such as AWS Redshift, Spark based data engineering solutions.
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ETL tools Teradata, MS SQL Server, Data Transformation Services (DTS) Export/Import, SQL Server Integration Service (SSIS), ADF, DataStage, Azure Synapse. Reporting tools SQL Server Reporting Services (SSRS), Power BI Additional Databases: Azure SQL Database, SQL Server Analysis Service (SSAS), EDW, EDL DevOps, Azure Blob, Azure Data Lake, Synapse, AWS, S3, GCP, Azure Functions.
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Develop in data technologies such as T-SQL, Microsoft Azure Data Factory, Databricks. Proficient in Databricks, Microsoft T-SQL, SSIS, ADF, Visio, Word, Excel, Outlook, PowerPoint and Azure DevOps (will train.
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Hands on experience working with Talend or SSIS as an ELT tool with Snowflake and SQL server Data Integration. The Hartford's Global Specialty IT team is seeking a hands-on Senior Staff Data Engineer to enhance and support its Data assets on snowflake and SQL server platform.
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You'll code with machine learning languages like DataBricks and Python Libraries to synthesize millions of data sets into patterns that answer ad hoc requests and then use Tableau for data visualization.
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Advanced experience in SQL in big data warehouse systems such as Snowflake, BigQuery, Databricks, etc. Experience in SQL and NoSQL, such as MySQL, PostgreSQL, Cassandra, HBase, Redis, DynamoDB, Neo4j, etc.
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What you’ll be responsible for:As a member of the Data Engineering - Business ETL team, you own the ETL/ELT pipelines and data warehouse that is used for financial and regulatory reporting.
ExpandApply NowActive JobUpdated 7 days ago - UpvoteDownvoteShare Job
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Experience with SQL and NoSQL based databases for storage and processing of structured, semi-structured & unstructured data. o Programming Language: Python, R, SQL, Java, Scala, Pyspark/Apache Spark, Shell scripting.
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Experience with data visualization tools, such as Tableau, Power BI, Google's Looker Studio, D3. Familiarity with R, Python, SQL, HTML, ArcGIS, and Drupal. The School of Government and Public Policy (SGPP) at the University of Arizona invites applications for a Data Architect III. The data architect will work closely with the Arizona Voter Project Director and academic collaborators to provide technical support for the Arizona Voter Project.
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Minimum RequirementsBachelor's degree in business administration, computer science, mathematics/statistics, engineering, management information science, data processing or related field and experience with Tableau, Cognos/Argos, SQL, and/or SAS is required.
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As a member of the Data Engineering - Business ETL team, you own the data warehouse and data pipelines that are used for financial and regulatory reporting. 4+ years of professional data engineering experience.
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Expertise in Python, R, SQL, statistics, data mining. 12+ years of experience working in the field of Data Engineering and Data Science. In this role, you will conduct sophisticated data analytics, data mining, exploratory analysis, predictive analysis, and statistical analysis.
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Applicants should also have a demonstrated understanding and experience using software and tools including big data tools like Kafka, Spark; relational SQL databases including Big Query or equivalent; workflow management and pipeline tools such as Airflow, and object function/object-oriented scripting languages including Scala, C.
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