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# Enterprise Data Architect

- **Company:** Gainsco Auto Insurance
- **Location:** Richardson, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Business Analytics Applications, Data Analysis, Business Intelligence Development, Cloud Computing, Continuous Integration, Data Architecture, Information Engineering, Data Files, Data Governance, Data Integration, Data Systems, Software Design Patterns, DevOps, Machine Learning, Metadata, Meta-Data Management, Microsoft SQL Server, OpenFlow, Cloud Services, Azure Data Lake, Data Streaming, Enterprise Data Management, Azure Data Factory, Snowflake, Technical Debt, Git, Infrastructure Automation Frameworks, Information Technology, Apache Kafka, Semantic Modeling, Terraform, Azure Synapse Analytics, Data Pipelines, Docker
- **Published:** October 19, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=aba14c9c72f7dcc5

## About the Role

Bachelor's degree in Computer Science, Information Technology, or a related field required., 10+ years of professional data engineering experience.
10+ years of experience in an enterprise Data Architect role, with responsibility for architecture strategy, standards, reference models, design patterns, or technical roadmaps.
5+ years of experience with Snowflake, Azure Data Factory, Azure Synapse, and SQL Server.
5+ years of experience with streaming, replication, or event-driven data technologies, such as Kafka, Openflow, Snowpipe, or comparable platforms.
5+ years of experience with Power BI and Tableau, including enterprise reporting architecture, semantic modeling, dataset design, dashboard governance, or analytics enablement.
Strong experience designing secure, scalable, reliable, and cost-efficient cloud data platforms.
Strong data governance experience involving metadata management, lineage, data quality, security, access controls, and regulatory compliance.
Experience applying DevOps methodologies, CI/CD workflows, ITIL practices, and Git-based development within enterprise data environments.
Experience communicating architecture standards, recommendations, and technical trade-offs to senior leadership and technical teams.

Other skills and abilities:

Familiarity with Terraform or other infrastructure-as-code tools.
Knowledge of containerization and orchestration technologies, including Docker and Kubernetes.
Exposure to data science workflows, machine learning platforms, or ML model integration.
Strategic and systems-level thinking with the ability to connect business priorities to enterprise data architecture.
Strong knowledge of modern data architecture, cloud platforms, data integration, modeling, analytics, and reporting.
Ability to design and implement solution patterns and reusable data products across enterprise environments.
Strong architecture review, technical decision-making, and problem-solving capabilities.
Ability to evaluate emerging technologies based on business value, risk, scalability, security, and cost.
Strong communication and presentation skills, including the ability to explain complex architecture concepts to non-technical stakeholders.
Ability to influence decisions, establish alignment, and lead cross-functional teams without direct authority.
Ability to mentor junior architects and engineers and strengthen architecture practices across teams.

## Description

Define and govern the enterprise data architecture vision, principles, roadmaps, reference models, and design standards.
Design scalable, secure, and resilient enterprise data platforms using Snowflake, Azure Data Factory, Azure Data Lake, Azure Synapse, SQL Server, Kafka, and cloud-agnostic technologies.
Establish solution patterns for batch, streaming, real-time, and event-driven data pipelines that support analytics and operational use cases.
Lead conceptual, logical, and physical data modeling strategies to strengthen consistency, performance, and reuse.
Set architecture and governance standards for metadata, lineage, data quality, security, access controls, semantic models, enterprise datasets, and self-service analytics.
Provide design reviews and technical guidance to Data Engineering teams while driving modernization, platform optimization, cost efficiency, and reduction of technical debt.
Communicate architecture direction, standards, and technical trade-offs to senior leaders, engineers, analysts, and business stakeholders.
Mentor junior architects and engineers while promoting consistent architecture practices across the organization..