Overview
On Site
Depends on Experience
Contract - W2
Skills
Amazon Web Services
Analytical Skill
Continuous Delivery
Continuous Integration
Customization
Cloud Computing
Collaboration
Communication
Conflict Resolution
Data Quality
Data Analysis
Data Architecture
Data Engineering
Data Governance
Data Security
Analytics
Apache Kafka
Development Testing
Enterprise Architecture
Finance
Artificial Intelligence
Innovation
Leadership
Meta-data Management
Metadata Modeling
Microsoft Azure
Microsoft Power BI
Salesforce.com
Snow Flake Schema
Orchestration
Microservices
Problem Solving
Regulatory Compliance
JSON
Lifecycle Management
Machine Learning (ML)
Management
Business Intelligence
Data Warehouse
DevOps
Database
Git
Agile
Stacks Blockchain
Storage
Job Details
Job Description:
Highly experienced and strategic Senior DataHub Architect to lead the design, implementation, and governance of our enterprise metadata and data discovery platform. This role is instrumental in shaping our data governance and cataloging strategy, leveraging DataHub as the core platform for centralized data, metadata management, lineage tracking, and data observability.
The ideal candidate will combine deep technical expertise with strategic vision and be comfortable working independently while also leading cross-functional teams across architecture, engineering, analytics, applications and governance functions.
The ideal candidate will combine deep technical expertise with strategic vision and be comfortable working independently while also leading cross-functional teams across architecture, engineering, analytics, applications and governance functions.
Key Responsibilities:
Architect the deployment and customization of DataHub, including storage, ingestion pipelines, and UI/UX extensions.
Integrate DataHub with a wide range of systems data warehouses, lakes, BI tools, data pipelines (e.g. Airflow), and third-party APIs.
Extend and customize DataHub metadata models and schema registries as needed.
Design lineage tracking and policy-driven metadata enrichment for structured and unstructured data assets.
Work independently with minimal oversight while guiding development and architecture teams toward delivery excellence.
Partner with data governance teams to ensure DataHub integration supports metadata harvesting, lineage tracking, and data discovery across platforms.
Build ingestion pipelines using Kafka, REST APIs, Python, and plug-ins to ingest metadata from tools like Snowflake, Power BI, Git, Tableau, etc.
Implement best practices for metadata lifecycle management, data quality monitoring, and catalog usage.
Work with security and compliance teams to ensure DataHub supports data protection, classification, and auditability.
Define and monitor key success metrics for catalog adoption, completeness, and trustworthiness.
Collaborate with integration architects to link DataHub with broader IT ecosystems (APIs, DevOps, security, CMS/microsites).
Guide data producers and consumers on how to tag, publish, and consume metadata effectively.
Guide teams through the delivery lifecycle: design, development, testing, deployment, and monitoring.
Review and approve technical designs, solution blueprints, and integration code for quality and alignment.
Collaborate with data and platform engineering teams to enable metadata-driven integration and discoverability using DataHub.
Manage relationships with vendor partners to ensure alignment with architectural standards and business goals.
Establish and maintain governance frameworks for team and vendor partners to ensure the effectiveness of architecture services.
Prepare and present weekly and monthly reports on architectural progress, challenges, and achievements.
Technologies and Platforms:
Cloud (AWS, Azure etc.)
Enterprise architecture
Software development
Infrastructure
Databases
DevOps practices
Microservices and containerization
Security and compliance
Data analytics and AI/ML
Mobile and web applications
Salesforce
Automotive
Captive finance
Cloud (AWS, Azure etc.)
Enterprise architecture
Software development
Infrastructure
Databases
DevOps practices
Microservices and containerization
Security and compliance
Data analytics and AI/ML
Mobile and web applications
Salesforce
Automotive
Captive finance
Qualifications:
Bachelor s or Master s degree in Computer Science, Information Systems, Data Engineering, or related field.
8+ years of experience in data engineering, data architecture, or metadata management.
3+ years of hands-on experience implementing and customizing DataHub, preferably in a complex enterprise environment.
Strong expertise in:
o Metadata standards and lineage frameworks
o Kafka, REST APIs, Python, SQL, and JSON-based data models
o Integration with modern data stacks (e.g., Snowflake, Airflow, Git)
Experience with CI/CD pipelines, containerization, orchestration, and cloud-native deployments (AWS, Azure, etc.).
Extensive experience across multiple domains and technologies.
Strong understanding of architectural principles and best practices.
Experience in captive finance, and data analytics sectors is preferred
Excellent leadership, communication, and interpersonal skills.
Proven ability to drive innovation and adopt new technologies.
Strong analytical and problem-solving skills.
Experience with containerization and orchestration
Experience with agile methodologies and DevOps practices is a plus.
Familiarity with other metadata is a plus.
Understanding of data privacy and compliance frameworks (GDPR, HIPAA, CCPA).
Experience in organizations with federated data ownership and large-scale data mesh or data fabric architecture.
Relevant certifications are highly desirable.
Bachelor s or Master s degree in Computer Science, Information Systems, Data Engineering, or related field.
8+ years of experience in data engineering, data architecture, or metadata management.
3+ years of hands-on experience implementing and customizing DataHub, preferably in a complex enterprise environment.
Strong expertise in:
o Metadata standards and lineage frameworks
o Kafka, REST APIs, Python, SQL, and JSON-based data models
o Integration with modern data stacks (e.g., Snowflake, Airflow, Git)
Experience with CI/CD pipelines, containerization, orchestration, and cloud-native deployments (AWS, Azure, etc.).
Extensive experience across multiple domains and technologies.
Strong understanding of architectural principles and best practices.
Experience in captive finance, and data analytics sectors is preferred
Excellent leadership, communication, and interpersonal skills.
Proven ability to drive innovation and adopt new technologies.
Strong analytical and problem-solving skills.
Experience with containerization and orchestration
Experience with agile methodologies and DevOps practices is a plus.
Familiarity with other metadata is a plus.
Understanding of data privacy and compliance frameworks (GDPR, HIPAA, CCPA).
Experience in organizations with federated data ownership and large-scale data mesh or data fabric architecture.
Relevant certifications are highly desirable.
Key Competencies:
Strategic Thinking: Develop and implement long-term technology strategies.
Leadership: Lead, mentor, and inspire a large team of architects.
Communication: Convey complex technical concepts to non-technical stakeholders. Ability to bridge business requirements with technical execution.
Innovation: Drive innovation and adopt new technologies.
Collaboration: Work effectively with cross-functional teams.
Problem-Solving: Address complex technical challenges.
Background in highly regulated environments
Self-motivated and accountable; thrives in fast-paced, high-autonomy environments.
Strategic Thinking: Develop and implement long-term technology strategies.
Leadership: Lead, mentor, and inspire a large team of architects.
Communication: Convey complex technical concepts to non-technical stakeholders. Ability to bridge business requirements with technical execution.
Innovation: Drive innovation and adopt new technologies.
Collaboration: Work effectively with cross-functional teams.
Problem-Solving: Address complex technical challenges.
Background in highly regulated environments
Self-motivated and accountable; thrives in fast-paced, high-autonomy environments.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.