Senior Enterprise Architect - Data Architecture

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The salary range for this position takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs.

At WGU, it is not typical for an individual to be hired at or near the top of the range for their position, and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is:

Job Description

We are seeking a visionary and detail-oriented Sr Enterprise Architect - Data Architecture to lead the design and implementation of advanced data ecosystems in alignment with data mesh principles, big data processing, streaming technologies, modern data governance practices, and AI/ML/Generative AI. In this role, you will collaborate with stakeholders across the organization to create a scalable, decentralized, and secure data architecture that supports strategic decision-making, AI/ML innovation, and advanced analytics.

This is a critical role in defining the technical blueprint for how data is collected, processed, stored, shared, and governed across multiple domains while ensuring the architecture scales to meet future needs.

Key Responsibilities

Data Architecture Design and Strategy

• Develop and maintain a comprehensive enterprise data architecture that aligns with business goals, ensuring scalability, flexibility, and performance.

• Create data models, pipelines, and storage solutions optimized for structured, semi-structured, and unstructured data.

• Standardize data integration patterns, defining the technical stack and best practices for the organization.

Implementing Data Mesh Principles

• Lead the transition from a centralized data approach to a domain-driven, decentralized data mesh architecture.

• Define and promote the concept of data as a product, ensuring domain teams own and manage their data with self-serve capabilities.

• Design cross-domain data contracts to ensure interoperability and governance.

• Partner with engineering teams to implement domain-oriented data ownership and infrastructure.

Big Data and Streaming Solutions

• Architect and implement scalable big data platforms using tools like Hadoop, Snowflake, BigQuery, or Redshift.

• Design real-time data streaming and event-driven architectures using tools like Kafka, Apache Flink, or Apache Pulsar.

• Build systems to process and analyze large datasets efficiently, optimizing for latency and throughput.

• Integrate real-time and batch processing solutions to create unified data platforms.

AI/ML and Generative AI Enablement

• Design data infrastructure to support AI/ML and Generative AI initiatives, ensuring data pipelines deliver high-quality training data.

• Collaborate with data scientists to define and implement data architectures for machine learning workflows, including feature engineering and model deployment.

• Enable scalable experimentation and deployment of Generative AI models like GPT, Stable Diffusion, or domain-specific LLMs (Large Language Models).

• Develop strategies for model monitoring, data drift detection, and retraining pipelines.

Data Governance, Reporting, and Security

• Establish and implement data governance frameworks, policies, and standards that ensure security, compliance, and privacy across the organization.

• Deploy tools for data cataloging, metadata management, and lineage tracking (e.g., Collibra, Alation, Amundsen).

• Define and implement data quality management processes, including monitoring, profiling, and validation tools.

• Architect and implement data reporting and visualization tools (e.g., Power BI, Tableau, Looker) to enable data-driven decision-making across the organization.

• Partner with the security team to implement robust data security protocols for encryption, access control, and threat prevention, ensuring compliance with regulations (e.g., GDPR, CCPA, HIPAA).

Collaboration and Leadership

• Work closely with data engineers, data scientists, analysts, and business stakeholders to understand data needs and deliver solutions.

• Drive the adoption of modern data architecture principles and best practices within engineering and business teams.

• Lead data workshops and training sessions to enable teams to effectively use and manage data products.

• Stay up-to-date with emerging technologies and industry trends to continually enhance the organization's data capabilities.

Qualifications

Required Skills & Experience

• Bachelor's or Master's degree in Computer Science, Data Engineering, or related field.

• Minimum of 10 years of experience in data architecture, engineering, or a related field.

• Hands-on experience with data mesh principles and implementing decentralized data architectures.

• Expertise in big data technologies (e.g., Hadoop, Spark, Hive, Presto, Snowflake, Redshift, BigQuery).

• Strong experience with streaming technologies such as Kafka, Apache Flink, Pulsar, or Storm.

• Proven expertise in building and scaling cloud-native data platforms (AWS, Azure, GCP).

• Deep understanding of data governance concepts, including metadata management, lineage tracking, and compliance tools.

• Strong knowledge of relational and NoSQL databases (e.g., PostgreSQL, MongoDB, Cassandra).

• Proficiency with programming languages (e.g., Python, Java, Scala) and data pipeline frameworks (e.g., Airflow, dbt).

• Experience integrating AI/ML workflows, MLOps into data architecture.

Certifications (Preferred)

Cloud Certifications:

• AWS Certified Data Analytics - Specialty

• AWS Certified Solutions Architect - Professional

Big Data and Streaming Certifications:

• Cloudera Certified Professional (CCP) Data Engineer

• Databricks Certified Data Engineer Associate/Professional

• Confluent Certified Developer for Apache Kafka

AI/ML Certifications:

• AWS Certified Machine Learning - Specialty

• TensorFlow Developer Certification

Data Governance and Reporting Certifications:

• Certified Data Management Professional (CDMP)

• Collibra Data Governance Certification

Preferred Skills

• Experience with infrastructure-as-code tools (e.g., Terraform, CloudFormation).

• Knowledge of container orchestration tools (e.g., Kubernetes, Docker).

• Familiarity with advanced analytics and machine learning platforms like Databricks, SageMaker, or Vertex AI.

• Experience working in highly regulated industries (e.g., finance, healthcare)

#LI-ZARD

Position & Application Details

How to Apply: If interested, an application will need to be submitted online. Internal WGU employees will need to apply through the internal job board in Workday.

Additional Information

Disclaimer: The job posting highlights the most critical responsibilities and requirements of the job. It's not all-inclusive.

Accommodations: Applicants with disabilities who require assistance or accommodation during the application or interview process should contact our Talent Acquisition team at recruiting@wgu.edu.

Equal Opportunity Employer: We are an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. #DEI