Enterprise Data Architect

The Enterprise Data Architect in this contract-to-hire role 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.

Preferred experience: 10+ years

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.

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

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

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)

Understanding of:

o Data lakes vs. lakehouses

o Distributed storage (S3, ADLS)

o Serverless vs provisioned architectures

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

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

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

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

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.)

Architecture Patterns & Design Skills

This is what separates senior from mid-level.

Designing:

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

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)

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

Metadata, Lineage & Cataloging

Critical for enterprise-scale environments.

Data lineage tracking (end-to-end)

Business glossaries

Metadata management systems

Impact analysis capabilities

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)