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Jobs or company...

Snow flake Data Engineer - Data Modeling

  • Full Time, onsite
  • Acadia Technologies, Inc.
  • On Site, United States of America
Salary undisclosed
Checking job availability...
Original
Simplified
Core Technical Skills:
    • SQL:
      Strong proficiency in SQL is essential for querying, manipulating, and managing data within Snowflake.
  • Data Warehousing:
    A deep understanding of data warehousing concepts, including star schemas, dimensional modeling, and data lake architectures is crucial.
  • ETL/ELT:
    Experience with Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) processes for building data pipelines is vital.
  • Cloud Platforms:
    Familiarity with cloud platforms like AWS and Azure, and the ability to leverage their services for data engineering tasks is important.
  • Data Modeling:
    Knowledge of data modeling techniques, including schema design, data relationships, and ensuring data integrity.
  • Data Pipelines:
    Experience in designing and building robust data pipelines for ingesting, transforming, and loading data into Snowflake.
  • Programming Languages:
    Proficiency in languages like Python or Java for data manipulation, scripting, and potentially building custom solutions.
  • Snowflake:
    Hands-on experience with the Snowflake data platform, including its features, tools, and best practices.
Other Important Skills:
  • Data Analysis:
    Ability to analyze data, identify patterns, and draw insights to inform business decisions.
  • Problem-solving:
    Strong analytical and problem-solving skills to troubleshoot issues and resolve errors in data pipelines.
  • Communication:
    Excellent communication and collaboration skills to work effectively with cross-functional teams.
  • Soft Skills:
    Project management, critical thinking, and the ability to adapt to evolving technologies are also valuable.
  • Data Governance and Security:
    Understanding of data governance principles and security best practices for protecting sensitive data.
  • Big Data Tools:
    Familiarity with big data technologies like Hadoop, Spark, and Kafka can be an advantage.
    BI Tools:
    Knowledge of BI tools like Tableau or Power BI for visualizing and analyzing data.
Optional but Helpful Skills:
  • Machine Learning: Basic understanding of machine learning concepts and their application in data engineering.
  • CI/CD: Experience with CI/CD pipelines for automating data pipeline deployments.
  • Data Visualization: Familiarity with data visualization tools for presenting insights to stakeholders.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
Report this job
Core Technical Skills:
    • SQL:
      Strong proficiency in SQL is essential for querying, manipulating, and managing data within Snowflake.
  • Data Warehousing:
    A deep understanding of data warehousing concepts, including star schemas, dimensional modeling, and data lake architectures is crucial.
  • ETL/ELT:
    Experience with Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) processes for building data pipelines is vital.
  • Cloud Platforms:
    Familiarity with cloud platforms like AWS and Azure, and the ability to leverage their services for data engineering tasks is important.
  • Data Modeling:
    Knowledge of data modeling techniques, including schema design, data relationships, and ensuring data integrity.
  • Data Pipelines:
    Experience in designing and building robust data pipelines for ingesting, transforming, and loading data into Snowflake.
  • Programming Languages:
    Proficiency in languages like Python or Java for data manipulation, scripting, and potentially building custom solutions.
  • Snowflake:
    Hands-on experience with the Snowflake data platform, including its features, tools, and best practices.
Other Important Skills:
  • Data Analysis:
    Ability to analyze data, identify patterns, and draw insights to inform business decisions.
  • Problem-solving:
    Strong analytical and problem-solving skills to troubleshoot issues and resolve errors in data pipelines.
  • Communication:
    Excellent communication and collaboration skills to work effectively with cross-functional teams.
  • Soft Skills:
    Project management, critical thinking, and the ability to adapt to evolving technologies are also valuable.
  • Data Governance and Security:
    Understanding of data governance principles and security best practices for protecting sensitive data.
  • Big Data Tools:
    Familiarity with big data technologies like Hadoop, Spark, and Kafka can be an advantage.
    BI Tools:
    Knowledge of BI tools like Tableau or Power BI for visualizing and analyzing data.
Optional but Helpful Skills:
  • Machine Learning: Basic understanding of machine learning concepts and their application in data engineering.
  • CI/CD: Experience with CI/CD pipelines for automating data pipeline deployments.
  • Data Visualization: Familiarity with data visualization tools for presenting insights to stakeholders.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
Report this job