Position Summary:
The Enterprise Data Architect is responsible for designing, implementing, and maintaining the overall data architecture of the organization. This role involves creating a comprehensive data strategy to support the business's strategic goals, ensuring data consistency, integrity, and availability across various systems. The ideal candidate will have extensive experience in data architecture, data modeling, and data management, with a strong understanding of business intelligence (BI), data analytics, Lakehouse architecture, and technology.
Key Roles & Responsibilities:
Data Migration Design and Technical Oversight:
- Discovery and Assessment - Understand what data exists and how it behaves.
- Migration Strategy & Planning - Define how migration will happen.
- Data Mapping and Transformation Design - Translate source data into target structures.
- Data Cleansing & Enrichment - Fix data before moving it.
- Migration Architecture & Pipeline Design - Design the technical movement of data.
- Data Migration Development & Testing - Build and validate pipelines.
- Data Reconciliation & Validation - Ensure migrated data is correct.
- Cutover Execution - Move into production.
Data Strategy Development:
- Develop and execute the enterprise data architecture strategy aligned with the organization’s goals.
- Collaborate with business leaders to understand data needs and ensure the architecture supports business objectives.
- Evaluate and recommend data management tools and technologies that align with the organization’s strategic vision.
- Implement master data management, reference data management, metadata management strategies to ensure data consistency, quality and security.
Data Governance and Compliance:
- Develop and Implement data governance policies and standards, as well as performance indicators and quality metrics, to manage data effectively and ensure compliance with data-related policies and standards.
- Monitor data quality and performance metrics, addressing issues as they arise to maintain data integrity.
Architectural Design:
- Design and implement data models, data flows, and data integration strategies to support business processes.
- Develop and maintain comprehensive data architecture documentation, including data models, data dictionaries, and metadata.
- Establish data governance frameworks and best practices to ensure data quality, consistency, and security.
Lakehouse Architecture:
- Design and implement Lakehouse architectures that combine the features of data lakes and data warehouses, optimizing for both structured and unstructured data.
- Utilize Lakehouse platforms and tools to integrate, store, and analyze large volumes of data efficiently.
- Evaluate and recommend Lakehouse solutions and technologies, including Delta Lake, Apache Hudi, MS Fabric, Databricks, or Apache Iceberg, to enhance data processing and analytics.
Business Intelligence (BI) Integration:
- Design and implement BI architecture to support reporting, analytics, and decision-making processes.
- Develop and maintain BI data models, dashboards, and reports that provide actionable insights to business stakeholders.
• Evaluate and recommend BI tools and technologies to enhance data visualization and analysis capabilities. •
Collaboration and Leadership:
- Lead cross-functional teams to drive data-related projects and initiatives.
- Communicate data architecture strategies and solutions to stakeholders at all levels, including executives.
- Mentor and provide guidance to junior data architects and data management staff.
Business Expertise:
Must-have
- Advanced SQL + data modeling
- Cloud data platform expertise
- ETL/ELT and pipeline design
- Data governance & security
Strong differentiators
- Real-time/event-driven architecture
- DataOps / automation
- Data mesh / modern architecture patterns
- AI/ML data infrastructure and application
- Data observability platforms
Problem Solving:
Data Architecture & Modeling
Core foundation skill
- Conceptual, logical, and physical data modeling
- Dimensional modeling (star/snowflake schemas)
- Normalization vs. denormalization tradeoffs
- Data vault modeling (increasingly important in modern architectures)
- Master Data Management (MDM) concepts
Tools
- ER/Studio, ERwin, Lucidchart, SQL DB tools
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Cloud Data Platforms (Critical Today)
Modern architectures are cloud-first.
- Deep expertise in at least one major cloud:
o Azure (Synapse, Data Factory, Fabric)
o AWS (Redshift, Glue, Lake Formation)
o Google Cloud (BigQuery, Dataflow)
o Data lakes vs. lakehouses
o Distributed storage (S3, ADLS)
o Serverless vs provisioned architectures
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Data Integration & Pipeline Design
Designing reliable data movement is central.
- ETL / ELT design patterns
- Batch and real-time streaming architectures
- Change Data Capture (CDC)
- API-based integration
- Event-driven architectures (Kafka, Event Hubs)
Tools
- Informatica, Talend, Azure Data Factory, dbt, Airflow, Python, Spark
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Databases & Storage Technologies
A senior architect should be multi-model.
- Relational databases (SQL Server, Oracle, PostgreSQL)
- NoSQL (MongoDB, Cassandra, DynamoDB)
- Data warehouse platforms
- Data lake / lakehouse architectures (Delta Lake, Iceberg)
Skills
- Query optimization
- Indexing strategies
- Partitioning
- Performance tuning
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Data Processing & Engineering
Hands-on understanding (even if not coding daily).
- SQL mastery (must-have)
- Python or Scala (for pipelines)
- Spark (critical for large-scale processing)
- Familiarity with distributed computing concepts
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Analytics & BI Ecosystem Understanding
Not just pipelines—how data is used.
- Data warehousing concepts
- Semantic layers and data marts
- BI tools (Power BI, Tableau, Looker)
- Query performance design for analytics workloads
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Data Governance, Security & Compliance
A major differentiator at senior level.
- Data governance frameworks
- Data lineage and metadata management
- Data catalog tools (e.g., Purview, Collibra, Alation)
- Security:
o Encryption (at rest/in transit)
o RBAC/ABAC
o Data masking / tokenization
- Regulatory awareness (GDPR, HIPAA, etc.)
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Architecture Patterns & Design Skills
This is what separates senior from mid-level.
o Data mesh vs data warehouse vs data fabric architectures
- Microservices & domain-driven design (data implications)
- Scalability and high-availability design
- Cost optimization patterns in cloud
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DevOps & DataOps
Modern data environments require automation.
- CI/CD pipelines for data (e.g., Azure DevOps, GitHub Actions)
- Infrastructure as Code (Terraform, ARM templates)
- Version control (Git)
- Monitoring & observability (data pipelines + quality)
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Data Quality & Observability
Ensuring trust in data.
- Data validation frameworks
- Data quality rules and monitoring
- Observability tools (Monte Carlo, Great Expectations)
- Root cause analysis of data issues
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Metadata, Lineage & Cataloging
Critical for enterprise-scale environments.
- Data lineage tracking (end-to-end)
- Business glossaries
- Metadata management systems
- Impact analysis capabilities
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Emerging & Advanced Skills (High Value)
Increasingly expected at senior levels.
- AI/ML data pipelines (basic understanding)
- Feature stores
- Real-time analytics
- Graph databases and knowledge graphs
- Data products (product thinking applied to data)
Nature & Area of Impact:
Business stakeholder interaction, decision making, and strategy definition.
Interactions / Interpersonal Skills: Describe the nature and level of interactions this job has with others, both internally and externally. Explain any specific interpersonal skills necessary to successfully perform this role (i.e., negotiation skills, represents business at external events or to governmental bodies, etc. ).
- Analytical Thinking: Strong analytical skills with the ability to design and implement complex data solutions.
- Problem-Solving: Excellent problem-solving skills with a proactive approach to resolving data issues.
- Communication: Effective communication skills, with the ability to present technical concepts to non-technical stakeholders.
- Leadership: Proven leadership abilities with experience in managing cross-functional teams and projects.
- Project Management: Strong organizational skills with experience in managing and delivering data projects on time and within budget.
