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Experience designing and maintaining ETL and ELT pipelines with technologies such as Spark, Airflow, Dagster, Prefect, Argocd, Metaflow, Kubeflow, etc. Leidos has an exciting opening for you as our next TS/SCI cleared Data Integration Engineer supporting the National Media Exploitation Center (NMEC) under our 10-year DOMEX Technology Platform (DTP) contract.
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Experience with SQL, NoSQL, and vector databases such as MSSQL, MySQL, PostgreSQL, Redis, FAISS, Milvus, Qdrant, etc. Perform Database builds, installs, configuration, administration, and troubleshooting of database systems (e.g., MariaDB/MySQL, Postgres, Elasticsearch, Qdrant, Milvus, etc.
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Data Integration Engineer - TS/SCI. Xcelerate Solution is seeking our next TS/SCI cleared Data Integration Engineersupporting the National Media Exploitation Center (NMEC) under our 10-year DOMEX Technology Platform (DTP) contract.
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Advanced knowledge and technical proficiency with ETL, cloud-based data warehousing and Apache Spark. 7-10 years of experience in Extract Transform and Load (ETL) Experience with Databricks, AWS and/or GCP, SQL, RDS, NoSQL, database design, programming languages - Scala, Java and/or Python.
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Ensure data integrity by performing employing data engineering best practices. Are you ready to join Leidos all-star team? Must possess an active TS/SCI clearance and the ability to obtain and maintain a TS/SCI with Polygraph.
$122,200 - $220,900 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Experience with distributed computer understanding and experience with SQL, Spark, ETL. 8+ years of experience developing data pipelines using modern Big Data ETL technologies like NiFi or StreamSets.
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And traditional database systems (i.e., SQL Server, Oracle, MySQL) Experience in implementing data pipelines for both streaming and batch integrations using tools/frameworks like Azure Data Factory, Glue ETL, Lambda, Spark, Spark Streaming, etc.
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You will also need familiarity with various open source database platforms and technologies, such as Hadoop, Spark, Kafka, other NoSQL and cloud based PaaS/SaaS data solutions e.g. AWS S3, Azure Data Lake. Experience with APIs and ETL/ELT are required with Data Fabric/Mesh a plus.
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Lead Software Engineer Backend (Python, SQL, ETL, Spark, AWS) Utilize programming languages like Java, Python, SQL, Node, Go, and Scala, Open Source RDBMS and NoSQL databases, Container Orchestration services including Docker and Kubernetes, and a variety of AWS tools and services.
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This architecture includes an Databricks, Microsoft Azure platform tools (including Data Lake, Synapse), Apache platform tools (including Hadoop, Hive, Impala, Spark, Sedona, Airflow) and data pipeline/ETL development tools (including Streamsets, Apache NiFi, Azure Data Factory.
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Key Skills: AWS, Spark, Adobe Analytics/AEP(Adobe Experience Platform) platform experience, Glue, Lamda, Python, Scala, EMR, Talend, PostgreSQL, Redshift. Configure AEP to get the data set needed and then use spark (AWS glue ) to load data in the data lake.
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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. 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.
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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. Knowledge of Qlik/Qlik Sense, QVD/QlikView, and Qlik Production Application Standards (QPAS) is a significant plus.
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3+ years experience with Big Data technologies, such asApache Hadoop, Hive or Spark, Databricks and/or Snowflake. Experience with Data Management methodologies and practices such as data warehousing, data integration (ETL/ELT), visualizations, metadata management, data security, data governance, data quality and analytics.
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Demonstrated experience with cloud technologies used with ETL and big data pipelines such as Glue, EMR, HIVE, Spark, S3, SQS, and SNS. Network programmability for automating network tasks (Ansible, Puppet, Chef, Python, Rest API’s.
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spark etl jobs in Washington, DC
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