Data Platform Architect

FutureSoft Consulting•Harrisburg, PA

15h

About The Position

We are seeking an experienced Data Platform Architect to lead the architecture, engineering, and evolution of a large-scale enterprise data platform supporting multiple business and technology teams. This is a senior technical leadership position focused on building reusable, secure, cloud-based data products and shared platform services. The Data Platform Architect will establish architectural standards, engineering practices, automation frameworks, security controls, and scalable platform capabilities that enable teams to rapidly develop modern data, analytics, digital, and AI solutions. The ideal candidate combines strong enterprise architecture expertise with hands-on platform engineering experience and has successfully built cloud data platforms from the ground up.

Requirements

Bachelor's degree in Computer Science, Information Systems, Software Engineering, Systems Programming, or a related discipline; equivalent relevant experience may also be considered.
10+ years of experience building large-scale cloud data platforms.
7+ years of hands-on experience in modern data architecture, platform engineering, and cloud infrastructure.
5+ years of experience designing and implementing enterprise security controls for cloud data platforms.
Strong experience with IAM, RBAC, encryption, audit logging, security monitoring, and cloud security architecture.
Proven hands-on experience building and operating cloud infrastructure using Terraform or equivalent Infrastructure-as-Code technologies.
Strong experience implementing modern DevSecOps practices.
Experience integrating security and governance requirements into cloud data platforms.
Strong enterprise data platform experience involving technologies such as: Snowflake, Databricks, AWS, Microsoft Azure, Terraform, Containerized applications, APIs and microservices, Modern cloud data services, CI/CD and DevSecOps tooling.

Nice To Haves

Candidates with experience building enterprise platforms from the ground up will be strongly preferred.
Designing enterprise data platforms supporting multiple organizations, departments, or business units.
Building reusable platform capabilities instead of application-specific solutions.
Designing secure multi-tenant cloud environments.
Supporting enterprise digital transformation or cloud modernization initiatives.
Designing platforms capable of supporting advanced analytics and artificial intelligence workloads.
Working closely with cybersecurity, CISO, governance, risk, and compliance teams.
Developing standardized onboarding and deployment patterns.
Designing cloud-native architectures using containers and automated infrastructure.
Establishing architecture governance and engineering standards across large organizations.

Responsibilities

Define the target architecture for a scalable enterprise data platform.
Develop a multi-year platform architecture and modernization roadmap.
Establish architecture patterns, technical standards, and reusable platform capabilities.
Evaluate emerging technologies while maintaining architectural consistency and operational sustainability.
Design enterprise data capabilities that can support multiple teams and organizations simultaneously.
Promote API-first, product-based, and reusable approaches to enterprise information sharing.
Design and build the initial MVP and continuously mature the enterprise cloud data platform.
Establish engineering standards, development practices, operational controls, and platform guardrails.
Architect secure, scalable, multi-tenant environments supporting multiple organizations and workloads.
Address data isolation, access control, security, and data co-mingling considerations within shared environments.
Design highly available and scalable cloud infrastructure supporting data, analytics, and AI workloads.
Implement cloud infrastructure using Infrastructure as Code, including Terraform or equivalent technologies.
Design reusable platform services rather than one-off implementations for individual teams.
Develop standardized onboarding patterns for teams adopting enterprise data products and services.
Build reusable data products and AI-ready infrastructure.
Design modern data ingestion, storage, processing, transformation, integration, and consumption patterns.
Partner with cloud, integration, application, analytics, and data engineering teams to deliver enterprise capabilities.
Support platforms and technologies such as Snowflake, Databricks, AWS, Azure, containers, APIs, and modern data services.
Design and implement enterprise security controls for cloud-based data environments.
Implement Identity and Access Management (IAM), Role-Based Access Control (RBAC), encryption, logging, auditing, and monitoring capabilities.
Collaborate with cybersecurity, governance, risk, compliance, and cloud engineering teams.
Integrate security controls directly into platform architecture and engineering workflows.
Establish clear security ownership boundaries across enterprise technology teams.
Implement and promote modern DevSecOps practices.
Champion a shift-left security approach by incorporating security controls throughout the software and infrastructure development lifecycle.
Automate infrastructure, security controls, deployments, and platform operations wherever possible.
Establish enterprise engineering and architecture best practices.
Provide technical leadership across multidisciplinary engineering teams.
Collaborate with architects, engineers, security teams, data teams, product teams, and executive technology leadership.
Translate enterprise strategy into practical architecture and engineering solutions.
Lead architecture decisions through influence and technical credibility rather than direct reporting authority.
Mentor technical teams on modern data architecture, cloud engineering, DevSecOps, and platform engineering practices.
Communicate complex technical concepts effectively to both technical and non-technical stakeholders.