Job Title: Enterprise Data ArchitectOverview / Summary We are seeking an experienced Data Architect to lead the design, implementation, and governance of enterprise-scale data platforms. The ideal candidate will define data architecture strategy, establish data standards, and collaborate with business stakeholders, data engineers, analytics teams, and solution architects to deliver scalable, secure, and high-performing data solutions across cloud and on-premises environments.
The role requires expertise in modern data platforms including Snowflake, Databricks, Microsoft Fabric, Azure, AWS, and Google Cloud, along with strong knowledge of data modeling, governance, data integration, and AI/ML enablement.
Key Responsibilities Enterprise Data Architecture - Define and maintain enterprise data architecture principles, standards, and best practices.
- Design scalable, secure, and resilient data platforms supporting analytics, AI/ML, and operational workloads.
- Create conceptual, logical, and physical data models.
- Develop enterprise data roadmaps aligned with business strategy.
Data Platform Design - Architect modern cloud-based data platforms using Snowflake, Databricks, Azure Synapse, Microsoft Fabric, AWS Redshift, and Google BigQuery.
- Design Lakehouse, Data Warehouse, and Data Mesh architectures.
- Build scalable ingestion, transformation, and serving layers.
Data Integration - Design batch, streaming, CDC, and API-based integration solutions.
- Architect ETL/ELT pipelines using dbt, Azure Data Factory, Apache Airflow, Informatica, Talend, and Fivetran.
- Establish reusable integration patterns and frameworks.
Data Modeling - Design star schema, snowflake schema, normalized, and dimensional models.
- Develop data marts for business domains.
- Optimize schemas for reporting and AI workloads.
- Define master data and reference data models.
Data Governance - Establish enterprise data governance standards.
- Define metadata management strategies.
- Implement data lineage and catalog solutions.
- Define data quality frameworks and KPIs.
- Ensure compliance with GDPR, HIPAA, CCPA, and other regulatory requirements.
Performance & Optimization - Optimize query performance and storage costs.
- Design partitioning, clustering, indexing, and caching strategies.
- Improve platform scalability and reliability.
AI & Advanced Analytics Enablement - Design data foundations for Machine Learning and Generative AI.
- Build feature stores and semantic layers.
- Enable real-time analytics and predictive modeling.
- Collaborate with Data Scientists and ML Engineers to support production AI solutions.
Stakeholder Collaboration - Work with business leaders to translate requirements into scalable data solutions.
- Partner with engineering teams to ensure architectural compliance.
- Provide technical leadership during solution design and implementation.
- Mentor architects, engineers, and developers.
Required Qualifications - Bachelor's or Master's degree in Computer Science, Information Systems, Data Science, or a related field.
- 10+ years of experience in Data Engineering, Data Warehousing, or Data Architecture.
- 5+ years of experience designing enterprise cloud data platforms.
- Strong understanding of enterprise architecture principles.
Technical Skills Cloud Platforms - Microsoft Azure
- AWS
- Google Cloud Platform
Data Platforms - Snowflake
- Databricks
- Microsoft Fabric
- Azure Synapse Analytics
- BigQuery
- Redshift
Programming - Python
- SQL
- PySpark
- Scala (preferred)
Data Integration - Azure Data Factory
- Apache Airflow
- dbt
- Informatica
- Talend
- Kafka
Databases - SQL Server
- Oracle
- PostgreSQL
- MongoDB
- Cosmos DB
Data Governance - Microsoft Purview
- Collibra
- Alation
- Unity Catalog
DevOps - Git
- Azure DevOps
- GitHub Actions
- CI/CD
- Terraform
Preferred Experience - Enterprise-scale cloud migration projects.
- Data modernization initiatives.
- Data Lakehouse implementations.
- Insurance, Healthcare, Financial Services, Retail, or Manufacturing domains.
- AI/ML platform architecture.
- Real-time streaming architectures.
- Multi-cloud environments.
Soft Skills - Strong communication and stakeholder management.
- Strategic thinking with a business-first mindset.
- Excellent analytical and problem-solving abilities.
- Ability to influence technical direction across multiple teams.
- Experience leading architecture reviews and design governance.
- Strong mentoring and leadership skills.
Success Measures - Enterprise data architecture adoption.
- High-quality, scalable, and secure data platforms.
- Reduced data delivery timelines.
- Improved data quality and governance compliance.
- Optimized cloud performance and cost.
- Increased business adoption of data and AI capabilities.
- Successful delivery of enterprise data transformation programs.
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