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Advanced experience in SQL in big data warehouse systems such as Snowflake, BigQuery, Databricks, etc. Advanced experience in SQL in big data warehouse systems such as Snowflake, BigQuery, Databricks, etc.
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Knowledge of data analysis tools and programming languages (e.g. Looker, Power BI, QuickSight, BigQuery, Azure Synapse, Python, R, or SQL) Knowledge of data analysis tools and programming languages (e.g. Looker, Power BI, QuickSight, BigQuery, Azure Synapse, Python, R, or SQL.
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Solid understanding of relational database modeling and design, including experience building data-intensive applications in data warehouse technologies like BigQuery, Snowflake or something similar.
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Experience with workflow orchestration management engines such as Airflow, Dagster, DBT, etcExperience with Cloud Services (AWS, Google Cloud, Microsoft Azure, etc). Our infrastructure – including USDC, a blockchain-based dollar – helps businesses, institutions and developers harness these breakthroughs and capitalize on this major turning point in the evolution of money and technology.
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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. Experience in SQL and NoSQL, such as MySQL, PostgreSQL, Cassandra, HBase, Redis, DynamoDB, Neo4j, etc.
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Proficiency in Google Cloud Platform (GCP) services relevant to machine learning and AI, such as AI Platform, BigQuery, Dataflow, and Tensorflow. Proficiency in Google Cloud Platform (GCP) services relevant to machine learning and AI, such as AI Platform, BigQuery, Dataflow, and Tensorflow.
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In-depth knowledge and hands-on experience in GCP cloud-native services such as BigQuery, Dataflow, Pub/Sub, Cloud Data Fusion, Cloud Composer and Google Data Studio etc. Experience in architecting and designing solutions leveraging services like Cloud Bigquery, Cloud Data Flow, Cloud Pub OR Sub, Cloud BigTable.
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Designing and developing large-scale data solutions using GCP services like DataProc, Dataflow, Cloud Bigtable, BigQuery, Cloud SQL, Pub/Sub, Cloud Data Fusion, Cloud Composer, Cloud Functions, Cloud storage, Compute Engine, Looker, Cloud IAM, etc.
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Keywords : GCP, Google Cloud Platform, BigQuery, Composer, SQL, Python, Data Engineering, Data Pipelines. Experience working with GCP technologies like BigQuery, Composer, GCS, DataStream, Dataflows.
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We are responsible for crafting the developer experience for Google Cloud Platform (GCP) developers in the Data Analytics space, including BigQuery, Bigtable, and others. Partner with product-aligned engineering teams to understand and improve the developer experience of our Google Cloud BigQuery and Pub/Sub client libraries.
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4+ years using cloud-based warehouse/data lake (Preferably in Azure Databricks metastores/catalogs or similar stacks such as AWS Athena/Redshift, GCP Bigquery, Azure ADLS, etc) 4+ years using cloud-based warehouse/data lake (Preferably in Azure Databricks metastores/catalogs or similar stacks such as AWS Athena/Redshift, GCP Bigquery, Azure ADLS, etc.
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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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Experience working with cloud based data warehouses like Google BigQuery, Redshift, or Snowflake. Experience working with cloud based data warehouses like Google BigQuery, Redshift, or Snowflake.
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Experience with SQL and cloud technologies like Snowflake, Google BigQuery, Databricks, presto etc., Experience with SQL and cloud technologies like Snowflake, Google BigQuery, Databricks, presto etc.
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The Analyst will use the Google Cloud Platform (GCP) BigQuery and Microsoft Power BI tools to access data about our customers. 3+ years of experience programming in SQL. Google Cloud Platform (GCP) BigQuery experience combined with Microsoft SQL Server or commensurate experience preferred.
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