Azure Data Architect

: Data and Cloud Solutions Architect

*NOTE:  Important- Candidate needs to have a Bachelor''s Degree in Computer science**
Linkedin information required in the coverpage
Resume should not be more than 3-4 pages


The Senior Cloud Data Architect is a hands-on role responsible for designing, evolving, and optimizing the organization’s cloud-based data architecture. This individual will shape the technical foundation for scalable, secure, and well-governed data systems that power analytics, AI, and enterprise intelligence.
 
As an individual contributor, the architect partners closely with data engineers, analysts, product teams, and cloud specialists to design end-to-end solutions—spanning ingestion, transformation, storage, metadata, and consumption. The ideal candidate brings deep technical expertise in data architecture, metadata design, and cloud-native data services, coupled with a keen ability to translate complex requirements into elegant, maintainable designs.
 
Core Responsibilities
 
Cloud Data Architecture & Strategy
 
•       Architect and optimize cloud-based data lakehouse and warehouse solutions that support analytics, machine learning, and enterprise integration needs.
•       Define scalable and reusable data frameworks for ingestion, curation, transformation, and consumption.
•       Evaluate and integrate Azure cloud services (e.g., Databricks, Data Lake, Event Hubs) to deliver high-performance data solutions.
•       Implement architectural standards that ensure consistency, interoperability, security, and compliance across the data environment.
•       Partner with engineering and business stakeholders to align architectural decisions with organizational objectives and KPIs.
•       Drive architectural reviews, proof-of-concepts, and recommendations for future-state cloud data patterns.
 
Metadata, Governance & Lineage
•       Design and operationalize metadata-driven architectures that improve discoverability, lineage tracking, and data quality monitoring.
•       Collaborate with governance and engineering teams to implement active metadata approaches, enabling dynamic data cataloging and lineage visibility across pipelines.
•       Define and enforce standards for metadata capture, schema management, and classification in alignment with enterprise data governance policies.
•       Integrate data catalog tools and frameworks (e.g., Unity Catalog, Purview, or Collibra) with cloud ecosystems for automated metadata flow.
•       Ensure consistent application of metadata structures across ingestion, transformation, and consumption layers.
 
Diagramming, Documentation & Technical Clarity
•       Produce detailed architecture artifacts, including data flow diagrams, system blueprints, and logical/physical data models.
•       Communicate technical concepts clearly through visualization tools like Lucidchart, Visio, or Draw.io.
•       Maintain robust documentation of architecture decisions, integration patterns, and system dependencies.
•       Support cross-functional collaboration by sharing architecture roadmaps and data lineage documentation.
 
Hands-On Implementation & Optimization
•       Contribute to the design and implementation of distributed data pipelines using Databricks, and Spark
•       Apply advanced optimization principles for performance, cost, and scalability across compute and storage layers.
•       Troubleshoot data latency, integrity, and transform issues across multi-environment pipelines.
•       Implement modernization best practices such as CI/CD automation, schema evolution management, and pipeline observability.
•       Partner with DevOps and platform teams to ensure maintainability and resilience of deployed solutions.
Qualifications
•       4-year Bachelor’s degree in Computer Science (strict requirement).
•       7+ years of professional experience in data engineering, architecture, or enterprise analytics platforms, including at least 3+ years focused on cloud data architecture.
•       Proven experience designing and implementing Azure-based data solutions, including Data Lake, Data Factory, Synapse, and Databricks.
•       Strong understanding of data modeling, schema design, and metadata management within large-scale data platforms.
•       Hands-on expertise with distributed data processing frameworks such as Spark and Databricks.
•       Demonstrated ability to produce and maintain clear architectural documentation and system diagrams.
•       Proficiency in Python and SQL for pipeline development, data transformation, and automation.
 
Preferred Qualifications
•       Experience implementing metadata catalogs and governance platforms (e.g., Unity Catalog, Purview, DataHub, or Collibra).
•       Proficiency with data lineage automation, provenance frameworks, and schema evolution tracking.
•       Familiarity with active metadata and modern data orchestration ecosystems (e.g., Airflow, dbt).
•       Knowledge of data security design patterns (e.g., encryption, RBAC, data masking).
•       Exposure to multi-cloud or hybrid architecture patterns integrating on-premise and cloud systems.
•       Understanding of medallion data architectures and modular data ingestion frameworks in large-scale deployments.
 
Additional Skills and Attributes
•       Analytical Mindset: Excels at identifying patterns, dependencies, and optimization opportunities within complex systems.
•       Technical Communication: Skilled at documenting and explaining architectural decisions with clarity and precision.
•       Collaboration: Works effectively across engineering, data governance, and cloud infrastructure teams without formal leadership responsibilities.
•       Execution Focus: Takes ownership of technical outcomes, ensuring architectural integrity from concept to production.


4-year Bachelor’s degree in Computer Science (strict requirement).
7+ years of professional experience in data engineering, architecture, or enterprise analytics platforms, including at least 3+ years focused on cloud data architecture.
Proven experience designing and implementing Azure-based data solutions, including Data Lake, Data Factory, Synapse, and Databricks.
Strong understanding of data modeling, schema design, and metadata management within large-scale data platforms.
Hands-on expertise with distributed data processing frameworks such as Spark and Databricks.
Demonstrated ability to produce and maintain clear architectural documentation and system diagrams.
Proficiency in Python and SQL for pipeline development, data transformation, and automation.


Skills: Data Architecture and Modeling