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Data Analyst-Data Modeler/Engineer-ETL-Source-to-Target Mapping

Georgia IT Inc.

  • Minnetonka, MN
  • 30+ days ago

    Highlights

    Utilizing preferred AI tools and platforms such as TensorFlow, Azure Machine Learning, scikit-learn, and PyTorch to build and deploy models that support enterprise analytics and innovation. Applying AI and machine learning techniques to enhance predictive analytics, automate decision-making, and uncover deeper insights from complex datasets.

    Numbers & Facts

    LocationMinnetonka, MN

    Description

    Position: Data Analyst-Data Modeler/Engineer-ETL-Source-to-Target Mapping
    Location: Minnetonka, MN (Hybrid- 3 Days in-office). ONLY LOCAL CANDIDATES ACCEPTED.
    Duration: 8-12 Months

    Reqd. Skills:
    • Data Engineering
    • ETL-Informatica
    • SQL/Oracle PL-SQL
    • Informatica PowerCenter
    • Data Modeling
    • Data Analysis
    • Snowflake or Azure or Kafka – Preferred
    • Healthcare Insurance

    Key Accountabilities:
    • Designing and implementing scalable data pipelines and storage solutions to support enterprise analytics.
    • Ensuring data quality, integrity, and security across all stages of the data lifecycle.
    • Collaborating with stakeholders to define data requirements and translate them into technical specifications.
    • Monitoring and optimizing performance of data systems and ETL processes.
    • Supporting the deployment and maintenance of data infrastructure in cloud environments.
    • Developing and maintaining complex SQL scripts and stored procedures for data transformation and reporting.
    • Leveraging Informatica IDMC for cloud-native data integration, data quality, and governance workflows.

    In addition to engineering responsibilities, the Data Generalist component of this role includes:
    • Performing exploratory data analysis and generating actionable insights.
    • Creating dashboards and visualizations using tools like Power BI, Tableau, or Excel.
    • Collaborating with cross-functional teams to align data efforts with business goals.
    • Automating data workflows using scripting languages such as Python or R.
    • Supporting business intelligence initiatives and translating data into strategic recommendations.
    • Documenting data processes and contributing to data governance standards.
    • Applying AI and machine learning techniques to enhance predictive analytics, automate decision-making, and uncover deeper insights from complex datasets.
    • Utilizing preferred AI tools and platforms such as TensorFlow, Azure Machine Learning, scikit-learn, and PyTorch to build and deploy models that support enterprise analytics and innovation.

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