Job Title: Senior Data Scientist Data Strategy & Governance

LOCALS ONLY!!!

Client: State of North Carolina NCDOT / NCDIT-T WEB
Job ID: 813224
Location: Raleigh, NC (Triangle area candidates only)
Work Arrangement: Mostly Remote / Hybrid
Contract Duration: October 19, 2026 October 18, 2027


State EXP is Plus

Location Requirements

• Candidates must currently reside in the Raleigh, Durham, or Chapel Hill, North Carolina area.
• The role is primarily remote, with occasional onsite meetings at NCDOT offices in the Triangle region.
• The selected candidate must attend the Raleigh office on the first day to collect equipment.

Job Overview

The North Carolina Department of Transportation (NCDOT) is seeking a Senior Data Scientist to support enterprise data strategy, governance, and architecture initiatives. The selected candidate will help establish consistent data management standards across business units and technical teams while supporting modern data platforms, including Azure Databricks and Azure Data Factory.
This role focuses on improving enterprise data quality, standardizing business definitions, documenting data lineage, and establishing reusable data management practices. The consultant will work closely with business stakeholders, data engineers, database administrators, application teams, and Power BI teams to ensure data is consistently defined, managed, transformed, and utilized across the organization.
The ideal candidate will combine strong enterprise data governance and data architecture expertise with practical knowledge of modern Azure data platforms and the ability to work independently in an evolving data strategy environment.

Key Responsibilities

• Establish and standardize enterprise data elements, business definitions, and business rules across multiple business units.
• Collaborate with business stakeholders and technical teams to identify authoritative data sources and improve consistency in data interpretation.
• Develop and implement data governance practices covering metadata management, data lineage, data quality, ownership, and stewardship.
• Document end-to-end data flows across source systems, applications, data platforms, transformation processes, and reporting solutions.
• Help maintain an inventory of enterprise data assets, including data owners, stewards, business definitions, and system relationships.
• Identify opportunities to standardize and reuse data ingestion, transformation, cleansing, and validation patterns using Azure Data Factory and Azure Databricks.
• Participate in data architecture and solution design discussions to promote scalable, consistent, and reusable data solutions.
• Work with data engineering, database administration, Power BI, application development, and project teams to address data management challenges.
• Translate business requirements into practical enterprise data standards, documentation, and governance processes.
• Establish foundational data management practices in an environment where formal governance frameworks may not yet exist.

Required Qualifications

• At least 3 years of experience defining and standardizing enterprise data elements, lineage, and business rules across business units.
• At least 3 years of experience collaborating with business stakeholders to translate data requirements into enterprise data standards.
• At least 3 years of experience implementing data governance practices, including metadata management, data lineage, and data quality.
• At least 3 years of experience documenting end-to-end data flows across systems and platforms.
• Strong understanding of enterprise data management, data governance, and data architecture principles.
• Experience working with modern data platforms, preferably Azure Databricks and Azure Data Factory.
• Excellent communication, documentation, analytical, and stakeholder collaboration skills.
• Ability to work independently and establish data standards and governance practices in a developing enterprise environment.

Preferred Technical Experience

• Azure Databricks and Azure Data Factory.
• Data ingestion, transformation, cleansing, and validation.
• Metadata catalogs, business glossaries, and data dictionaries.
• Data quality frameworks and data lineage documentation.
• Enterprise data architecture and reusable data management patterns.
• Power BI and enterprise reporting environments.