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It is mandatory to have a strong knowledge of Looker and BigQuery. Must have good hands on Looker, Spotfire and SQL Use Looker to create a metadata repository (LookML), views, and dashboards.
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Build integrations with tools throughout the data pipeline ecosystem including ETL tools (e.g. Airflow), warehouses (e.g. Snowflake), transformation tools (e.g. dbt), and business intelligence tools (e.g. Looker.
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Independently translate client requests into dashboards and reports in common BI tools, such as: Tableau, Looker Studio or Power BI (we most commonly use Tableau and Looker at PMG) 2+ years of experience in pulling data from tools such as Tableau or Looker Studio for reporting purposes.
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Create effective reporting and dashboards by applying expertise in data visualization tools such as Mixpanel, Quantum Metric, Looker/Tableau, and Splunk to monitor product performance. You will be responsible for building out a holistic view of performance as well as a detailed analytics roadmap across the entire Product Lifecycle by leveraging state of the art analytics tools (i.e. SQL/Hive/Hadoop/Cloud, Mixpanel, Quantum Metrics, Tableau/ThoughtSpot/Looker, etc.
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Familiarity with GCP / BigQuery / Looker Studio. You will be leading development and optimization efforts for all marketing and revenue growth related data pipelines, which then need to be integrated into Looker Studio for advanced data visualization.
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In this role, you’ll be helping us to drive our PLG motion forward, surfacing insights from a wealk of our customer data, partnering directly with our growth teams on Marketing & R&D to build out our experimentation capabilities, and owning our data stack (Looker, DBT, Fivetran, Snowflake, Census.
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You have hands on experience with data visualization and reporting tools such as Tableau, R Shiny, and/or Looker. You will be responsible for building out a holistic view of seller risk performance, identifying potential risks and supporting controls by leveraging analytics insights and tools (i.e., SQL/Hive/Hadoop/Cloud, Adobe Analytics, VisualIQ, Tableau/ThoughtSpot/Looker, etc.
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Knowledge and experience with typical CS SaaS tech stack such as SFDC, Gainsight, Marketo, Looker, and Gong with specific expertise in Salesforce and Gainsight. Experience using and developing reports, metrics and dashboards with Salesforce, Looker or other relevant business intelligence tools.
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Very strong experience in BI Tools (GCP Looker/Tableau/PowerBI/SAP Business Objects). BI Visualization Engineer (Tableau/PowerBI/SAP BO/GCP Looker) with strong SQL skills. Must Have: GCP Looker ,BI, Business Intelligence, Tableau, PowerBI, SAP BO, Looker, SQL, Data Modelling.
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Tableau, Airflow, Looker, Mode, Prefect. Advanced proficiency with SQL and data visualization tools (e.g. Tableau, Looker, etc) Spiral (formerly Square Crypto) builds and funds free, open-source Bitcoin projects.
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DTS Senior Data Analyst - Hackensack Meridian Health - Looker Jobs. Working knowledge of Business Intelligence products (i.e. Looker, Tableau, QLik, etc.) More Looker Jobs. DTS Senior Data Analyst - Hackensack Meridian Health - Looker Jobs.
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Data visualization (e.g. Tableau, Looker, MicroStrategy) Production experience with: HDFS, YARN, Hive, Spark, Kafka, Oozie / Airflow, Amazon Web Services (AWS), Docker / Kubernetes, Snowflake. Data visualization (e.g. Tableau, Looker, MicroStrategy.
Full-timeRemoteExpandApply NowActive JobUpdated 9 days ago - UpvoteDownvoteShare Job
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Experience with data translation tools (DBT/Google Cloud Dataform) data visualization tools Looker/Looker Studio/Tableau. Experience with data translation tools (DBT/Google Cloud Dataform) data visualization tools Looker/Looker Studio/Tableau.
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Support our data infrastructure which includes Snowflake, dbt, Tableau, Looker, and GCP services. Experience with visualization/reporting tools like Tableau, Looker, Power BI. Support our data infrastructure which includes Snowflake, dbt, Tableau, Looker, and GCP services.
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Familiarity with data exploration and data visualization tools like Tableau, Looker, etc. Experience deploying and maintaining pipelines (AWS, Docker, Airflow) and in engineering big-data solutions using technologies like Databricks, S3, and Spark.
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