Enterprise Data Architect

Loftware External•Portsmouth, NH

About The Position

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.

Requirements

Advanced SQL + data modeling
Cloud data platform expertise
ETL/ELT and pipeline design
Data governance & security
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
Deep expertise in at least one major cloud: Azure (Synapse, Data Factory, Fabric), AWS (Redshift, Glue, Lake Formation), or Google Cloud (BigQuery, Dataflow)
Understanding of: Data lakes vs. lakehouses, Distributed storage (S3, ADLS), Serverless vs provisioned architectures
ETL / ELT design patterns
Batch and real-time streaming architectures
Change Data Capture (CDC)
API-based integration
Event-driven architectures (Kafka, Event Hubs)
Relational databases (SQL Server, Oracle, PostgreSQL)
NoSQL (MongoDB, Cassandra, DynamoDB)
Data warehouse platforms
Data lake / lakehouse architectures (Delta Lake, Iceberg)
Query optimization
Indexing strategies
Partitioning
Performance tuning
SQL mastery (must-have)
Python or Scala (for pipelines)
Spark (critical for large-scale processing)
Familiarity with distributed computing concepts
Data warehousing concepts
Semantic layers and data marts
BI tools (Power BI, Tableau, Looker)
Query performance design for analytics workloads
Data governance frameworks
Data lineage and metadata management
Data catalog tools (e.g., Purview, Collibra, Alation)
Security: Encryption (at rest/in transit), RBAC/ABAC, Data masking / tokenization
Regulatory awareness (GDPR, HIPAA, etc.)
Designing: 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
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)
Data validation frameworks
Data quality rules and monitoring
Observability tools (Monte Carlo, Great Expectations)
Root cause analysis of data issues
Data lineage tracking (end-to-end)
Business glossaries
Metadata management systems
Impact analysis capabilities
AI/ML data pipelines (basic understanding)
Feature stores
Real-time analytics
Graph databases and knowledge graphs
Data products (product thinking applied to data)
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.

Nice To Haves

Real-time/event-driven architecture
DataOps / automation
Data mesh / modern architecture patterns
AI/ML data infrastructure and application
Data observability platforms
ER/Studio, ERwin, Lucidchart, SQL DB tools
Informatica, Talend, Azure Data Factory, dbt, Airflow, Python, Spark
Power BI, Tableau, Looker
Purview, Collibra, Alation
Azure DevOps, GitHub Actions
Terraform, ARM templates
Monte Carlo, Great Expectations

Responsibilities

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.
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.
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.
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.
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.
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.
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.