Data Scientist / Data Architect / Data Governance Lead

ABOUT THE COMPANY:

Bluebird Fiber is a premier fiber telecommunications provider of internet, data transport, and other services to carriers, businesses, schools, hospitals, and other enterprises in the Midwest. To learn more, please visit bluebirdfiber.com.

Join an amazing team of telecommunication professionals! Bluebird is a dynamic growing company in need of a Data Architect to be a part of a collaborative team. This is a full-time, benefit eligible position in our Kansas City Office. All of us at Bluebird work hard to meet objectives for the organization and live the mission and values of this growing company to meet a common goal. Check out this video that highlights our amazing company culture.

JOB SUMMARY:

We are seeking a highly skilled and strategic Data Architect to lead our data governance, security, and management initiatives. This senior role will be responsible for designing and implementing the organization's enterprise data architecture, ensuring that our data is secure, reliable, and accessible for business-critical functions. The ideal candidate is a proactive leader who can define data strategy, enforce best practices, and collaborate with cross-functional teams to align our data ecosystem with business goals.

REQUIRED QUALIFICATIONS:

• Bachelor's or master's degree in Computer Science, Information Technology, or a related technical field.
• 10 years of hands-on experience in data architecture, data modeling, and data governance, with a proven track record of designing and implementing complex data ecosystems. Experience working in regulated industries is a plus.
• Proven experience (8 years) designing and implementing enterprise-level data architectures.
• Extensive experience with data modeling, data warehousing, and modern data platforms (e.g., cloud environments like AWS, Azure, or GCP).
• Deep expertise in data modeling, data warehousing, database technologies (SQL, NoSQL), big data technologies (e.g., Spark), and modern cloud platforms (e.g., AWS, Azure, GCP).
• Deep expertise in data governance and security principles, including regulatory compliance frameworks.
• Strong knowledge of how to structure data for machine learning and AI workloads, including experience with MLOps platforms.
• Hands-on experience with data classification and data cataloging tools (e.g., Collibra, Alation).
• Excellent communication, interpersonal, and leadership skills, with the ability to influence and build consensus across the organization.

PREFERRED QUALIFICATIONS:

• Professional certifications in data architecture, data governance, or cloud platforms.
• Experience with big data technologies (e.g., Hadoop, Spark).
• Familiarity with data integration and ETL/ELT frameworks.

KEY RESPONSIBILITIES:

Data Governance, Strategy and Architecture

• Define and drive the organization's overall vision, data strategy, roadmap, and architecture vision. This includes the data AI architecture vision, strategy, and roadmap. This includes the design of scalable data lakes, data warehouses, and data fabric architectures.
• Establish and enforce data governance policies and standards to ensure data quality, consistency, and compliance with all relevant regulations (e.g., GDPR, CCPA). Lead the implementation of a comprehensive data governance framework, including data quality management, data lineage tracking, and master data management (MDM). Collaborate with data owners and stewards across business units to establish clear roles, responsibilities, and accountability for data assets.
• Establish clear rules and policies governing the responsible usage of data within AI and ML models, including documentation of data lineage for model training. Design data infrastructure specifically optimized for AI workloads, including data pipelines for machine learning models, and architect solutions for large language models (LLMs). Develop bias mitigations strategies to ensure diverse and representative datasets to prevent AI biases, and architect monitoring systems for model drift.
• Evaluate, recommend, and select appropriate data management technologies, including cloud platforms (e.g., AWS, Azure, GCP), storage solutions, and governance tools.
• Architect complex data integration patterns to connect disparate data sources across the organization, ensuring seamless data flow and a unified data view.

Data Security and Privacy

• Design and implement a robust data security architecture to protect sensitive data from unauthorized access, breaches, and corruption.
• Develop security protocols, such as encryption, access controls (IAM), and masking techniques to safeguard data in transit and at rest.
• Conduct regular security audits and vulnerability testing to identify gaps in security architecture and develop remediation plans.
• Ensure the data architecture and its supporting systems are compliant with internal policies and external data protection regulations.

Data Modeling and Management

• Design and maintain conceptual, logical, and physical data models for transactional and analytical systems.
• Oversee the development of database schemas, metadata management, and data cataloging efforts to improve data discoverability and understanding.
• Define and standardize data architecture components, including storage solutions (data lakes, warehouses, etc.), data pipelines, and integration patterns.
• Evaluate and recommend new data technologies, tools, and platforms that align with the organization's strategic needs.

Data Classification

• Design and implement a robust data security architecture, including controls for access management, encryption, and data masking to protect sensitive information.
• Create and manage an organization-wide data classification scheme based on data sensitivity and importance (e.g., public, internal, confidential, restricted).
• Implement technical controls and processes to automatically classify and tag data assets, ensuring proper handling and security.
• Collaborate with business and legal teams to define and apply data classification rules consistently.

Team Collaboration and Leadership

• Provide technical guidance and mentorship to data engineers, analysts, developers, and other IT teams on best practices for data management and security.
• Work closely with business stakeholders to understand their data requirements and translate them into effective architectural solutions.
• Foster a data-centric culture across the organization, promoting awareness and understanding of data governance principles.