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Data Modeler

Job in Madison, Dane County, Wisconsin, 53774, USA
Listing for: Next Level Business Services, Inc.
Full Time, Part Time position
Listed on 2026-01-12
Job specializations:
  • IT/Tech
    Data Engineer, Data Analyst
Job Description & How to Apply Below
Overview

Position Title: Data Modeler

Location: Hartford/ Windsor, CT

Hire Type: Full Time Only

Responsibilities
  • Needs to know dimensional and/or operational/3NF/transactional modeling.
  • Needs to be able to focus heavily on data dictionary and mapping document work.
  • With XML schema designing skills (using ACORD framework) in addition to modeling.
  • Must be very good at profiling sources and mapping them into the target model.
  • Should have worked on any modeling tool like Erwin, Rational etc.
  • Excellent communication to interact with the business customers.
  • Property & Casualty Insurance domain experience is nice to have.
  • Leverage any Industry Data Models (Framework) Eg IBM Insurance Model (IIW; IAA), Retail Data Warehouse.
  • Must know the industry standard logical naming methods, Metadata management process.
Experience & Qualifications

Experience

Required:

7 years

Will be directly working with clients and offshore counter-parts.

Skills & Pre-screening Questions
  • Generic Managerial

    Skills:

    Need good verbal/written communication/analytical skills.
  • OLTP Design knowledge.
  • ODS/3NF Relational Modeling.
  • Leverage any Industry Data Models (Framework) Eg IIW, IBM Retail Data Warehouse.
  • Rate your Oracle/Erwin skills.
  • Data profiling knowledge (Any tools: Trillium; Oracle DQ tool).
  • What kind of large projects you have been handling? Roles?
  • Relevant Experience (number of years).
  • Interaction/collaboration process with external (BA, Architect, Development) teams?
  • Tell me about data modeling in ODS?
  • Which subject areas you worked on ODS subject area.
  • Availability for Interviews.
  • Data analysis, normalized, 3NF.
  • Transient database management, account management; part day on a daily basis.
  • Irrespective nature of data, how do you handle data loads?
  • How did you separate old records from new records to track the timing of the transaction?
  • How would you identify the latest transaction in an ODS atomic/OLAP dimension?
  • Time-variant / time-invariant surrogate keys? Link latest single instance of the customer with history data in ODS?
Additional Information

All your information will be kept confidential according to EEO guidelines.

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