Please Note: This is a Hybrid Position with two days remote and 3 days onsite.


About the Position:

The Hybrid Data Architects/Engineers will provide Data Warehouse Architect/Engineer services to serve as the
primary resource(s) responsible for designing and building a data warehouse solution and working with
conventional data warehouse technologies to devise plans that support designing data warehouse solutions
in alignment with the Client's initiatives.

Candidate shall be responsible for the following:
  • Participation in all aspects of DW and ETL process design, creation, and specification for components.
  • Providing the following services in coordination with technical, functional and management representatives from Client and the AOC.
  • Source Data Model to DW Logical Design ERD
    • Understanding the general structure of the MDEC data and star schema models currently in use by Client, and any related data sources identified as a result of initial planning and assessments.
    • Creating a logical warehouse data design.
    • Collaboration with platform administration team, design an ETL process to move data from the source system to the data warehouse, including, but not limited to, the following:
      • Outlining the ETL process, setting the borders of data processing.
      • Providing system architecture for each element and the whole data pipeline.
      • Documenting the requirements of the system, manage its development and facilitate necessary knowledge transfer.
      • Assisting in the actual development/implementation of ETL tools.
      • Conducting testing of the tools and data pipelines.
  • DW Schema
    • Developing effective DW model(s) representing the data entities of the logical design based on functional analytic, reporting and bulk data requirements
  • DW Physical Design
    • Collaborating with platform administration to assist their efforts in creating a DW Physical design including technical considerations for data quality and operational efficiency.
  • Collaborating with business and technology stakeholders to ensure data warehouse architecture
  • development and utilization.
  • All work completed by the proposed resource(s) shall be completed within regulatory compliance
  • standards to protect sensitive data.
  • Reporting as follows:
    • Weekly progress report on programs and project,
    • Weekly report communicating project progress and status,
    • Weekly time reporting on Client provided forms, and
  • Any additional reports as assigned by the supervising manager.


Key Required Tools/Skills:
  • Power BI
  • Tableau
  • Azure Synapse
  • Azure Data Factory
  • Azure Blob Storage
  • Azure Databricks
  • Microsoft Fabric
  • One Lake


Requirements

Basic Qualifications and Skills:

Candidate should be possessing the following preferred skills, experience, and capabilities:
  • Bachelor's degree with at least 5 years of experience
  • Database and analytical skills.
  • Ability to query source data.
  • Knowledge and experience with ETL tools, Visual Studio, and transmitting and reconstructing Extensible Markup Language (XML) and Structured Query Language (SQL).
  • Knowledge of scripting languages, such as Python, and the ability to automate repetitive tasks in Azure.
  • Strong analytical, consultative, and communication skills; as well as the ability to make good judgment and work with both technical and business personnel.
  • Ability to:
    • Work productively and maintain effective working relationships with peers, end users, vendor development staff, and all levels of management and Judicial personnel.
    • Critically think and problem solve,
    • Provide excellent communication and mentoring needs,
    • Quickly evaluate, learn and prototype new technologies.
    • Write optimized SQL queries and manage databases, as Azure data analysts frequently interact with Azure SQL database and other SQL-based services.
  • Experience with:
    • BI best practices, relational structures, dimensional data modeling, structured query language (SQL) skills, data warehouse and reporting techniques.
    • Dimensional modeling, STAR schema design, Snow fake schema design, slowly changing dimensions, confirmed dimensions.