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Experience with cloud data processing and analytics services such as AWS Glue, Google Dataflow, Apache Spark on cloud platforms, or Azure Data Lake Analytics. Familiarity with cloud data storage solutions such as AWS S3, Google Cloud Storage, Azure Blob Storage, AWS Redshift, Google BigQuery, Azure SQL Data Warehouse, or others.
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Engineer cloud-based analytics solutions on AWS, Azure, Databricks, GCP, Snowflake, Oracle, Informatica Cloud and a combination of native cloud technologies, including computing at edge and curating data-in-motion.
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Assist in physical audits of products and verification of data in Google Sheets, NAV and Metrc. Google Sheets data entry and adjustments. PurposeThis position is responsible for compliantly organizing starting materials in METRC, NAV and Google Sheets for the Extractions, Kitchen & Processing Packaging Teams.
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Data Stack: Fivetran, Airbyte, dbt, Snowflake, Looker, Hex, Google Analytics 4, Google Tag Manager. As a Senior Data Analyst/Data Analyst, you’ll dive deep into our product analytics, email, SMS, and sales data to inform digital and product strategy to drive retention and LTV.
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Master's Degree in related field (e.g., Data Science, Predictive Analytics, Machine Learning, Statistics, Applied Mathematics, Computer Science) Bachelor's Degree in related field (e.g. Data Science, Predictive Analytics, Statistics, Marketing Analytics, Applied Mathematics, IT.
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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. Expertise in open source data science technologies such as Python, R, Spark, SQL.
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Collaborating amongst team members across several geographies, our Cloud practitioners engineer cloud-based analytics solutions on AWS, Azure, Databricks, GCP, Snowflake, Oracle, Informatica Cloud and a combination of native cloud technologies, including computing at edge and curating data-in-motion.
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Experience with Customer Data Platforms (CDP) like Segment, mParticle, Tealium AudienceStream or other industry leading CDP technology would be a plusHands-on experience with marketing platforms such as Google Analytics, Adobe Analytics, Marketo, HubSpot, Salesforce Marketing Cloud, etc would be a plusFamiliarity with cloud platforms such as AWS, Azure, or Google Cloud, and their data services (S3, Redshift, BigQuery, etc.
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Experience working with cloud based data warehouses like Google BigQuery, Redshift, or Snowflake. Bachelor’s or Graduate degree, Field of Studies required: In a quantitative field or commesurat experience such at Statistics, Business Analytics, Data Science, Applied Math, Econometrics or Operations Research.
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The Processing Analytics Supervisor will be responsible for ensuring that the Processing Analytics team has correctly entered data across all data systems (Google sheets, Smartsheets, METRC, Biotrack & NAV.
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5+ years experience leading, managing and delivering Oracle cloud-based data and analytics technology engagements end-to-end with resources in multiple locations. 6+ years experience with relevant technology consulting experience to include: enterprise-scale technical experience with cloud and hybrid infrastructures for data and analytics, architecture designs, migrations, and technology management.
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Reporting chops using combination of Google Analytics (GA4), Excel, SEMrush, Hubspot, Google Data Studio, Pardot (Salesforce), Marketo, Tag Manager, (roughly in this order of importance.
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Website metrics (Google Analytics (GA4 / Crazy Egg) all the way through to CRM metrics (lead quality and ROI in HubSpot, Marketo, Pardot [Salesforce]). At present, clients range from deep tech data analytics, AI/ML, storage, cloud infrastructure, cybersecurity to fintech, health tech and climate tech.
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Set up of network ad accounts to ensure correct audience targeting, ad structure and conversion event tracking through Google Analytics (GA4), Tag Manager, Ad Networks, and into CRM systems (HubSpot, Marketo, Pardot [Salesforce.
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Understand and interrogate data via Google BigQuery (SQL) and Google Analytics (GA4). The analytics team also collaborates closely with the data science team, working together on model development and using algorithms to address core questions.
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