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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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QUALIFICATIONS:Advanced MS SQL Server knowledge (MS SQL Server 2016+ preferred)Development expertise with MS SQL Server Integration Services (SSIS), dbt and Apache AirflowProficient with cloud DW and ETL/ELT solutions (e.g., Snowflake, BigQuery, Talend, Matillion, etc.
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High level of proficiency in ETL processes and demonstrated, hands-on experience with technologies such as Databricks, Python, Spark, Scala, JavaScript/JSON, SQL, and Jupyter Notebooks. Data Engineer McLean, VA, Crystal City, VA, Mark Center or Pentagon (Hybrid, 2-3 Days On-Site, rotating location) Permanent/Direct Hire Clearance - Active Secret Clearance or Interim Secret Required Our client is seeking an experienced Data Engineer to join our Advana team.
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Minimum of 5+ years of experience in designing ETL/ELT solutions using tools like Informatica, DataStage, SSIS , PL/SQL, T-SQL, etc. Minimum of 5+ years of experience in managing data assets using SQL, Python, Scala, VB.NET or other similar querying/coding language.
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5+ years of experience in creating Source to Target Mappings and ETL design for integration of new/modified data streams into the data warehouse/data marts. Leverage combination of ETL/ELT methodologies to pull complex relational and dimensional data to support loading DataMart s and reporting aggregates.
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Minimum of 2+ years of experience with Epic Clarity and/or Caboodle data models or with Cerner Millennium / HealthEintent and experience using Cerner CCL. Experience supporting data pipelines that power analytical content within common reporting and business intelligence platforms (e.g. Power BI, Qlik, Tableau, MicroStrategy, etc.
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Exposure to Amazon Redshift, Amazon S3, Hadoop HDFS, Azure Blob, or similar big data storage and management components. The Director of Data Engineering will also partner with the broader OAS Analytics organization to harness the power of client data to facilitate analytical insight and will be responsible for building quality and efficiency into every project.
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Client is seeking a Data Engineer with health care experience to implement and manage cross-domain, modular, flexible, scalable, secure, reliable and quality data solutions that transform data to support analytics and insight generation for our clients.
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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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Support the full data engineering lifecycle including research, proof of concepts, design, development, testing, deployment, and maintenance of data management solutions. Minimum of 5+ years of combined experience in data engineering, ingestion, normalization, transformation, aggregation, structuring, and storage.
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Data Pipeline Architecture : Design and implement scalable and reliable data pipelines within AWS cloud environments, leveraging technologies such as Snowflake, SQL Server, Oracle, Python, Apache Airflow, SSIS, DataStage, or Informatica.
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Utilize knowledge of various data management technologies to drive data engineering projects. Leverage data pipeline monitoring tools to detect data integrity issues before they result into user visible outages or data quality issues.
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Implement, modify, and maintain data integration efforts that improve data efficiency, reliability, and value. The Data Engineer will implement, test, deploy, monitor, and maintain the delivery of data in a systematic method and will support a wide variety of analytical needs for our customers.
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Build and run ETL pipelines to onboard data into the platform, define schema, build DAG processing pipelines and monitor data quality. Strong experience working with SQL and databases/engines such as MySQL, PostgreSQL, SQL Server, Snowflake, Redshift, Presto, etc.
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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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