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Job Description

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- 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).

:

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

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