Data Governance Specialist

Job Title: Data Governance Specialist

 

Location:

[Location] (Opportunities for remote/hybrid/flexible work available)

 

Reports to:

CDO / Head of Analytics or Business Intelligence, CTO, CTO, Potentially Risk or Compliance Experts.

 

Role Purpose

At [Company Name], we lead the way in artificial intelligence by building cutting-edge products that help organizations harness advanced data insights. We offer a flexible work setting where new ideas are welcomed and rewarded. As a Data Governance Specialist, you will be at the core of our data management strategies, making certain that our AI models and platforms rely on secure, compliant, and high-quality data. This role is essential for supporting ethical AI development, protecting sensitive information, and ensuring data accuracy across the organization.

 

Company Overview

[Company Name] is a trailblazer in the AI industry, focused on delivering innovative solutions that empower clients to make meaningful data-driven decisions. Our team values collaboration, integrity, and an inclusive culture where everyone is encouraged to share insights. We’ve earned accolades for our supportive work environment and commitment to professional growth. By integrating modern tools and frameworks, we remain at the forefront of AI technology and pride ourselves on offering engaging career opportunities where you can refine your skills and contribute to groundbreaking projects.

 

Key Responsibilities

• Design and Maintain Data Governance Frameworks

  • Develop comprehensive policies and standards to manage the data lifecycle, ensuring alignment with regulatory guidelines (e.g., GDPR, CCPA) and internal protocols.

  • Oversee the creation and management of data dictionaries, taxonomies, and metadata repositories that support AI model training and development.

• Ensure Compliance and Security

  • Verify that data usage in AI applications adheres to industry regulations and internal data security measures.

  • Work closely with cybersecurity and legal teams to mitigate risks related to data handling and privacy.

• Data Quality Management

  • Define benchmarks for data accuracy and consistency across multiple AI systems, including machine learning pipelines and data analytics platforms.

  • Monitor and remediate data quality issues, partnering with data engineers and data scientists to ensure high standards for critical datasets.

• Cross-Functional Collaboration

  • Collaborate with product owners, machine learning engineers, and data analysts to align data governance objectives with business requirements.

  • Champion data governance best practices throughout the organization, facilitating training and workshops.

• Security and Compliance

  • Enforce security best practices across AI environments, from data encryption at rest and in transit to identity and access management.

  • Ensure systems comply with relevant standards or regulations (e.g., GDPR, HIPAA) where applicable.

• Collaboration and Documentation

  • Work closely with Data Scientists and Machine Learning Engineers to understand model requirements, experiment tracking, and deployment strategies.

  • Document processes, workflows, and system architectures for transparency and consistency among teams.

  • Present findings, metrics, and recommendations to leadership and other technical groups.

• Innovation and Best Practices

  • Stay informed about the latest AI DevOps and MLOps methods, tooling, and industry trends.

  • Identify potential improvements to existing workflows and propose enhancements to tools or technologies used.
     

 

Required Skills and Qualifications

• Educational Background

  • Bachelor’s degree (or higher) in Computer Science, Engineering, or an equivalent technical field, or demonstrable professional experience.

• DevOps Expertise

  • Proficiency in version control (Git) and continuous integration (Jenkins, GitLab, or similar).

  • Experience orchestrating containerized workloads using Docker and Kubernetes.

• AI/Machine Learning Domain Knowledge

  • Experience working with at least one major ML framework (TensorFlow, PyTorch, or scikit-learn).

  • Familiarity with data preprocessing, model training, and model evaluation workflows.

• Cloud Services and Infrastructure

  • Hands-on experience with AWS, GCP, or Azure for AI and big data workloads (e.g., EC2, S3, IAM, GCS, Cloud Dataflow).

  • Comfort with Infrastructure as Code (Terraform, AWS CloudFormation) and ability to manage production-level environments at scale.

• Automation and Scripting

  • Strong scripting abilities in Python, Bash, or similar languages.

  • Ability to automate repetitive tasks, integrations, and deployments for both infrastructure and ML pipelines.

• Security and Reliability

  • Understanding of best practices for security, including network segmentation, encryption, and secure configuration.

  • Familiarity with incident response processes and building high-availability systems.

• Problem-Solving and Communication

  • Capable of diagnosing production-level issues under time constraints, coordinating closely with various teams to find solutions.

  • Skilled at explaining technical concepts to both technical and non-technical audiences, facilitating effective collaboration.
     

Preferred

• Experience with GPU-based workloads, distributed ML training, or HPC environments.

• Knowledge of data versioning solutions (e.g., DVC, MLflow) and experiment tracking.

• Exposure to Agile or Scrum methodologies for project management.

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Perks and Benefits:

Clearly outline the benefits and perks of the role.

 

How to Apply:

End with a strong call to action encouraging candidates to apply. Include a direct link to the application page and provide contact information for further queries.

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• Salary Information: Provide a clear salary range to maintain transparency and meet legal requirements.

• Privacy Policies: Protect candidate privacy by following all applicable data protection and privacy laws.

• Equality & Non-Discrimination: Include an equal opportunity statement to uphold our commitment to a diverse, inclusive workplace that does not discriminate based on race, gender, age, disability, or any other protected characteristic.

• Accessibility: Make reasonable accommodations available for candidates with disabilities and include information on how they can request assistance throughout the hiring process.

• Environmental and Social Responsibility: If your company has sustainability initiatives or community engagement programs, mentioning them briefly can attract candidates who prioritize working for socially responsible employers.

• Transparent Hiring Process: Briefly explain the hiring process (e.g., “Our interview process typically includes three stages: an initial screening, a technical interview, and a final interview”) to help candidates know what to expect.