Enterprise Data Architect

GAINSCO•Richardson, TX

5d•Hybrid

About The Position

GAINSCO is seeking an Enterprise Data Architect to define and govern its enterprise-wide data and analytics architecture strategy. As a subject matter expert, this role establishes the long-term vision, standards, reference models, and architectural patterns that guide how data is ingested, integrated, stored, modeled, governed, and delivered across the organization. The architect partners with business leaders and Data Engineering, Analytics, Security, and Governance teams to design and implement scalable, secure, and reusable data solutions. The role guides modernization, shapes enterprise data products, and presents architecture standards to senior leadership to support an optimized, secure, and stable data environment. The position sets architecture direction for enterprise data platforms, pipelines, modeling, governance, and analytics. It provides design reviews and technical guidance to Data Engineering teams, promotes modernization and platform efficiency, communicates technical trade-offs to stakeholders, and mentors junior architects and engineers.

Requirements

Bachelor’s degree in Computer Science, Information Technology, or a related field.
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 in enterprise data environments.
Experience communicating architecture standards, recommendations, and technical trade-offs to senior leadership and technical teams.
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 explaining complex architecture concepts to non-technical stakeholders.
Ability to influence decisions, establish alignment, and lead cross-functional teams without direct authority.

Nice To Haves

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.
Ability to mentor junior architects and engineers and strengthen architecture practices across teams.
Strategic and systems-level thinking, with the ability to connect business priorities to enterprise data architecture.

Responsibilities

Define and govern 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 supporting analytics and operational use cases.
Lead conceptual, logical, and physical data modeling strategies to improve 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 technical-debt reduction.
Communicate architecture direction, standards, and technical trade-offs to senior leaders, engineers, analysts, and business stakeholders.
Mentor junior architects and engineers and promote consistent architecture practices across the organization.