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    Data Architect - Milwaukee, United States - Compunnel Inc.

    Compunnel Inc.
    Compunnel Inc. Milwaukee, United States

    1 week ago

    Default job background
    Accounting / Finance
    Description

    Job Title: Data Architect

    Location: Milwaukee, WI - Hybrid – 2 to 3 days work from office

    Type: Direct inhouse requirement - Contract-to-hire with a contract period of minimum 1 year with possibility of extension.

    Interview Process: Please note these are all going to be Video Conference and if the candidate is comfortable, we can arrange an in-person interview as well.

    • First - 30 mins with some of their inhouse SMEs
    • Second - 45 – 60 mins Screen Share round where they will provide a live activity to work on.
    • Third round 15 – 20 mins with reporting manager – just cultural alignment and expectation setting.

    Current needs for a Data Architect:

    • Excellent leadership capabilities.
    • Cloud experience. Primarily in Snowflake/DBT/ Fivetran
    • Traditional on-prem SQL Server data warehousing architecture, design, modeling. (3NF)
    • Snowflake and related Snowpark toolset
    • Fivetran
    • DBT
    • Python
    • PowerBI Service and PowerBI on-prem
    • Azure Private vnet and Private Links

    What we look for:

    • Seeking candidates with approximately years of experience in Data Solution delivery in a complex environment working collaboratively in a team setting
    • This is a hybrid opportunity in the Milwaukee area with 2-3 days onsite per week at our Headquarters.
    • Proficient in Data Solution tools and concepts such as:
    • Business Intelligence tools: Alteryx, Microsoft tools (SQL Server Management Studio, SSRS, SSAS, SSIS, Power Pivot, Power Query, PowerBI)
    • Database: SQL Server, Azure SQL Database, Azure Data Lake, Azure Synapse
    • Data Query tools: SQL, T-SQL, Azure Data Studio
    • Data Modeling: ER/Studio Data Architect, 3NF and dimensional modeling
    • Data Integration tools: SSIS, Azure Data Factory, Azure Synapse Pipelines
    • Data Warehousing concepts: Inmon, Kimball, Data Lake, Data Lakehouse
    • Data Integration concepts and strategies: EII, ETL, EL-T, SOA, and EAI
    • Reference Data Management
    • Data Management and Quality: data mapping, data profiling, data catalog, relational data modeling, master data management, data governance
    • Data Formats: csv, pipe-delimited, JSON, XML, Parquet.
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