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Extensive experience with Bigquery or similar cloud warehouses (Snowflake, Redshift) Advanced working SQL knowledge and experience working with Cloud columnar databases (Google BigQuery, Amazon Redshift, Snowflake), query authoring (SQL) as well as working familiarity with a variety of databases.
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Mentor and guide junior data engineers in the team Drive and implement automation to empower the organization to scale efficiently and with flexibility Keep up to date with new technologies and methodologies related to data engineering and apply them to improve the data platform Requirements: Bachelor or equivalent 1+ years working Experience in AWS Redshift, Databricks, BigQuery, Oracle, or Graph databases.
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Familiarity with SQL & noSQL databases: Amazon Redshift / BigQuery / Amazon Athena / MongoDB. Familiarity with SQL & noSQL databases: Amazon Redshift / BigQuery / Amazon Athena / MongoDB.
$29.75 - $36.25 an hourInternExpandApply NowActive JobUpdated 12 days ago - UpvoteDownvoteShare Job
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Experience with modern columnar data warehouses such as Snowflake, Redshift, BigQuery. Driving Results: Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using Python, SQL (using dbt for transformation), commercial SaaS and OSS offerings, and the entire AWS suite of technologies.
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Experience with data warehousing (e.g., Databricks, Snowflake, Redshift, BigQuery, or similar) Experience with data warehousing (e.g., Databricks, Snowflake, Redshift, BigQuery, or similar.
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2+ years of experience working within data warehouse tools such as RedShift and BigQuery (both will be used in this role at PMG), and the ability to work interchangeably within the two. Our team is made up of over 500 employees globally, and our work for brands like Apple, Athleta, Best Western Hotels & Resorts, Kohler, McDonald's, Nike, Old Navy, Sephora and Shake Shack runs across 85+ countries and has received top industry recognition from Cannes Lions to Adweek Media Plan of the Year.
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Experience with big data technologies (Domo, Snowflake, BigQuery, Redshift, Hive etc.) Experience with big data technologies (Domo, Snowflake, BigQuery, Redshift, Hive etc.) 3-5+ years experience with Business Intelligence Software (Domo, Power BI, Tableau, Looker, Sisense, etc.
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Demonstrated SQL experience with tools like BigQuery, RedShift, or Snowflake. Demonstrated SQL experience with tools like BigQuery, RedShift, or Snowflake. Implementing data pipelines built in Apache Airflow on the Google Cloud Platform (Google Composer.
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Hands-on experience with database systems (e.g. SQL, NoSQL, vector, search) and data warehousing solutions (e.g. Snowflake, Redshift, BigQuery) Experience with cloud platforms such as AWS, Azure, or GCP, and related services (e.g. S3, Snowflake, BigQuery.
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Experience with SQL, data visualization tools, and cloud-based analytics platforms highly preferred (Snowflake, Databricks, Redshift, BigQuery) Experience with SQL, data visualization tools, and cloud-based analytics platforms highly preferred (Snowflake, Databricks, Redshift, BigQuery.
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3+ years of hands-on experience using any of the Data warehouses like Snowflake, AWS Redshift, BigQuery, or Teradata. 3+ years of hands-on experience using any of the Data warehouses like Snowflake, AWS Redshift, BigQuery, or Teradata.
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Hands-on development experience with Relational DBs (MS SQL Server, PostgreSQL, Oracle), as well as complex stored procedures and functions using SQL and optimization of SSIS packages and Redshift database queries.
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Expertise in Cloud Data Warehouses in Redshift, BigQuery or analogous architectures a plus. Presto, Hive, SparkSQL, Cassandra, or Solr other Big Data query and transformation experience a plus.
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Experience designing and building data pipelines using APIs, BigQuery and Streaming ingestion methods. As a Senior Data Engineer on the MMA National IT Data and Analytics Team, we will count on you to develop and maintain scalable data pipelines, enhance data models, and provide subject matter expertise in data acquisition and consumption pipelines on Azure, Databricks and Confluent.
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Solid understanding of DevSecOps, Git integration, Code deployment using CI/CD pipelines, managing work in an agile environment using Azure Boards, Confluence. Learn more about what makes us different and how you can thrive as a Senior Data Engineer at MMA National.
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