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Associate Cloud Data Engineer - Remote. Data Clymer has been a fully remote company since day one, so we will never ask you to commute to work. SQL: 1+ Years (Preferred) Data Warehousing (Snowflake, Databricks, Redshift, BigQuery, Clickhouse, etc.
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Build data ingestion processes using Google Cloud ETL tools (Data fusion, Dataflow, Dataproc, Airflow) We are currently seeking a Data Scientist to join our team in a Hybrid/Partially on-client site in Auburn Hills, Michigan (US-MI), United States (US.
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Position DescriptionADP is hiring a Principal Data Scientist. Nice to have: Experience with Tableau or other visualizationFamiliarly with Deep Learning, Neural Network or LLM modelsExperience with ETL tools like Snaplogic, Mulesoft, Informatica and/or Stitch.
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This is a fully remote position requiring extensive experience in data conversion, SQL, ETL processes, and proficiency in utilizing tools like JIRA. The ideal candidate is a Baltimore resident with 15+ years of experience with data conversion, with prior experience with CGI to Oracle Cloud migrations, SQL, and ETL. Bonus points for experience with the HR and Payroll functionalities of CGI.
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Data movement (real-time replication or ETL/ELT), multiple clouds (AWS, Azure Google, Alibaba), cloud analytics destinations (Snowflake, Databricks, Kafka, RDS, Synapse, BigQuery, 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 in performing ETL and data engineering work by leveraging multiple google cloud components using Dataflow, Data Proc, BigQuery. Streaming ETL: Apache Beam, Kafka.
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As an ETL/ELT Pipeline Engineer, you will be responsible for designing and building efficient data integration pipelines using GCP native tools or open-source technologies such as Python and PySpark.
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The Lead Data Scientist will formulate Glo’s data strategy and work with the Data Engineer to implement the appropriate architecture and execute on the strategy. Experience with data visualization tools such as Tableau, MixPanel, Google Looker.
Full-timeExpandApply NowActive JobUpdated 10 days ago - UpvoteDownvoteShare Job
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Strong knowledge of Extraction, Transformation and Loading (ETL) processes using frameworks like Azure Data Factory, Synapse, Databricks, Informatica by gathering requirements from the stake holders or analyzing the existing code and perform enhancements or new development.
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ADP is hiring a Senior Lead Data Scientist. 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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Implement ETL design patterns for data warehousing using Kimball methodology and master data management, with expertise in T-SQL scripting, query optimization and data governance.
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Experience: 5 years of technical experience performing ETL development; developing and scheduling batch jobs; developing UNIX Shell Scripts for data validation and reconciliation; designing relational and dimensional databases; performing data integration in AWS using Python and HIVE; developing Python data integration and validation scripts; and performing real-time data integration using messaging queue(MQ.
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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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CI/CD orchestration (Jenkins, CircleCI, or TravisCI)ETL, data modeling, cloud-based data storage, processingGCP Data Services (Dataflow, BigQuery, Dataproc)Workflow and data pipeline orchestration (Airflow, Oozie, Jenkins, etc)Container orchestration technologies (Kubernetes, OpenStack, Docker swarm, etc)Open source software involvementMonitoring/Logging with technologies like Splunk, ElasticSearch, Logstash/Fluentd, Stackdriver, Time-series databases like InfluxDB etc.
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