Data Modeler

Geologics•Sterling Heights, MI

1d•Hybrid

About The Position

GeoLogics is seeking a Data Modeler for a hybrid role in Sterling Heights, MI, working with a top Aerospace and Defense client. The position involves designing and implementing data models, collaborating with various stakeholders, maintaining data dictionaries, optimizing data infrastructure, and ensuring data security and quality. The role also includes supporting configuration control and documentation for the business's data architecture model, identifying Authoritative Source of Truth (ASOT) databases, and managing data replication to cloud environments.

Requirements

Excellent oral and written communications.
Be directly involved in developing data models and process flows as well as liaising with multiple stakeholders - mainly engineering, to document and/or validate the current state or As-Is business process.
Proven ability to work within teams in which your contributions are critical to success.
Proficiency with tools such as Power BI, Visual Basic, MSExcel, MATLAB/Simulink, Mathcad, MagicDraw, Midas, AWS/AWS Redshift, SSAS, Alation, Dataiku, Oracle, SQL.
Experience using entity-relationship diagrams (ERDs), dimensional modeling, normalization techniques, and metadata repositories to design and document data structures.
Understanding of data warehousing concepts.
Experience with big data technologies like Hadoop or Spark.
Familiarity with cloud-based data services such as AWS or Azure.
Background in data governance and data quality concepts.
Excellent problem-solving and analytical skills.
Strong communication and collaboration skills with both technical and non-technical stakeholders.
Experience in Agile or Scrum environments.
Develop Data Models
AWS Medallion architecture including AWS Redshift
Data Cataloguing tools like Collibra
Data Transformation tools like Dataiku
Dashboarding tools like PowerBI or Tableu
Experience using entity-relationship diagrams (ERDs)
Big Data tech like Hadoop or Spark

Nice To Haves

Bachelors in Data Management, Data Science, Math, Statistics

Responsibilities

Design and implement conceptual, logical, and physical data models to represent organizational data accurately.
Collaborate with business analysts, data architects, developers, and other stakeholders to gather requirements and ensure models meet business and technical needs.
Maintain a central data dictionary, enforce data standards, and ensure compliance with data governance policies.
Optimize performance of data infrastructure by tuning data models for efficiency, reduce redundancy, optimize reusability at the right levels and improve database performance.
Design solutions for integrating data across systems and perform reverse engineering of existing databases when necessary.
Develop and maintain documentation, naming conventions, and coding standards to ensure consistency across data systems helping the business to define 'what does good look like?'
Ensure that data is stored securely, efficiently, and in a manner that is accessible for analysis and reporting.
Ensuring the accuracy, consistency, and reliability of data, implementing data validation and quality control processes.
Optimize data pipelines for speed, scalability, and performance, ensuring that data is available when and where it's needed.
Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and ensure that data infrastructure meets their needs.
Troubleshoot issues with data infrastructure, monitor system performance, and perform regular maintenance to ensure smooth operation.
Support configuration control, documentation and change impact analysis on the businesses data architecture model including identification of Authoritative Source of Truth (ASOT) databases, connectors to replicate data from ASOTS to Midas and/or AWS Cloud Environments through data transformation within Power BI.