Enterprise Data Solution Architect

The
Enterprise Data Solution Architect
is responsible for defining and implementing the organization's enterprise-wide data strategy and architecture. This role ensures that data assets are structured, governed, integrated, and leveraged effectively across all cloud environments. The ideal candidate has deep experience designing modern data architectures on
AWS, Microsoft Azure, and/or Google Cloud Platform (GCP)
, and can guide business and technology leaders in building scalable, secure, and high-performing data ecosystems.

Key Responsibilities

1. Data Strategy & Architecture

• Develop and maintain the
  enterprise data architecture roadmap
  , aligning with business strategy and digital transformation goals.
• Define
  data principles, standards, and best practices
  for modeling, integration, storage, and access.
• Design
  modern data architectures
  , including data lakes, data warehouses, lakehouses, and real-time streaming pipelines.
• Ensure data architectures enable
  advanced analytics, AI/ML, and BI
  capabilities across the enterprise.

2. Cloud Data Enablement

• Architect and implement
  cloud-native data solutions
  using AWS (Redshift, Glue, S3, Athena), Azure (Synapse, Data Factory, Databricks), or GCP (BigQuery, Dataflow, Pub/Sub).
• Guide the migration of on-premise data assets to cloud environments, ensuring performance, security, and compliance.
• Optimize data processing pipelines for
  cost efficiency, scalability, and resilience
  across multi-cloud environments.

3. Data Governance & Security

• Collaborate with data governance teams to ensure adherence to
  data quality, metadata management, and lineage tracking
  standards.
• Implement
  data privacy and compliance frameworks
  (GDPR, CCPA, HIPAA, etc.) in alignment with enterprise policies.
• Define access control models and encryption strategies for sensitive data.

4. Collaboration & Leadership

• Partner with
  enterprise architects, data engineers, and business stakeholders
  to deliver end-to-end data solutions.
• Provide architectural oversight on projects involving
  data integration, analytics platforms, and AI initiatives
  .
• Mentor data engineering and analytics teams in adopting
  best practices and architectural patterns
  .

5. Emerging Technologies & Innovation

• Independently Evaluate new data technologies, Well architected Frameworks, and tools to enhance enterprise data capabilities.
• Recommend approaches for
  data mesh, data fabric, or federated data architectures
  as the organization evolves.

Qualifications Required:

• Bachelor's degree in Computer Science, Information Systems, Data Science, or related field.
• 12+ years
  of experience in data architecture, data engineering, or enterprise architecture roles.
• Proven experience architecting solutions on at least
  two major cloud platforms
  (AWS, Azure, GCP).
• Expertise in
  data modeling (conceptual, logical, physical)
  and
  ETL/ELT pipeline design
  .
• Strong proficiency in
  SQL, Python, and cloud data orchestration tools
  .
• Deep understanding of
  data warehousing, streaming, and big data ecosystems
  (e.g., Databricks, Snowflake, Kafka).

Preferred:

• Master's degree or relevant certifications (e.g., AWS Certified Data Analytics – Specialty, Azure Data Engineer Associate, GCP Professional Data Engineer).
• Familiarity with
  metadata management, data catalogs, and data governance tools
  (Collibra, Alation, Informatica, etc.).
• Experience with
  data mesh/fabric concepts
  and enterprise-scale
  API/data service design
  .
• Excellent communication and leadership skills, with the ability to translate complex data concepts for business stakeholders.

Key Competencies

• Strategic thinking and enterprise-level systems design
• Cross-platform cloud expertise
• Strong understanding of security and compliance in data management
• Ability to drive alignment between business needs and technology solutions
• Continuous learning and innovation mindset