Platform Architect Jobs
Platform Architect jobs are open across financial services, healthcare, cloud infrastructure, and enterprise software, from senior engineer to principal and distinguished architect, with specializations in cloud-native design, API integration, and data platform architecture. Find a role that fits from the openings below and apply directly.
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About NovaSource
NovaSource Power Services is the world’s #1-ranked solar operations and maintenance (O&M) provider and insight-driven total asset optimization partner for renewables asset owners ready to fuel smart growth. With over 20 years of operating experience and a presence on 5 continents, NovaSource has the global reach and strategic capabilities to achieve our clients’ renewables goals around the world.
NovaSource’s comprehensive approach to total asset optimization in addition to O&M services includes value engineering, performance analysis, strategic supply chain management, and advanced monitoring systems. The company operates in key global markets managing over 40GW of solar power plants. NovaSource’s expertise extends beyond solar and includes battery energy storage systems (BESS), offering a complete suite of services for the evolving renewable energy landscape.
Position Overview
We are seeking a hands-on Data & Analytics Platform Architect to serve as the technical authority for our enterprise data platform — designing, building, and continuously evolving the systems that power contractual, operational, analytical, and AI-driven workloads across the organization. This role combines strategic architecture with deep engineering ownership: you will lead the evolution of our Azure and Databricks-based data ecosystem, refine our multi-layer data pipelines, implement data mesh principles across multiple repositories, and drive high levels of automation to ensure a reliable, scalable, and cost-efficient platform. You will also explore and integrate emerging technologies — including AI/LLM capabilities — to enhance the platform’s intelligence and business value. Strong collaboration, commitment to incremental delivery, and the ability to mentor technical teams are essential.
This position follows a hybrid working schedule, with a combination of remote work and in-office collaboration.
Key Responsibilities
Enterprise Data Platform Architecture & Engineering
- Architect, build, and continuously improve the enterprise data platform, ensuring reliability, scalability, and maintainability across core business processes and analytics use cases.
- Own the full data platform lifecycle — from schema design and pipeline architecture to monitoring, performance tuning, and incident response.
- Establish and enforce data modeling standards, naming conventions, and governance frameworks across all environments.
- Implement policy enforcement points and access controls (data catalogs, encryption, RBAC) to ensure compliance, privacy, and data protection.
Analytical Data Modeling & Schema Design
- Design, build, and evolve dimensional data models — including star schemas on Azure Databricks — optimized for analytics and reporting.
- Develop and refine medallion architecture (bronze-silver-gold layers) for efficient data ingestion, transformation, and consumption.
- Balance model simplicity, flexibility, and performance while minimizing redundancy across analytical datasets.
Cloud & Big Data Architecture
- Lead the design and evolution of the Databricks intelligent data platform, enabling scalable big data processing and laying the foundation for AI/ML capabilities.
- Architect and manage Azure-based infrastructure including Azure SQL, Azure Data Factory, Azure Synapse Analytics, Data Lake, and related services.
- Apply data mesh principles across multiple data repositories to enable decentralized, domain-oriented data ownership.
Pipeline Optimization & Automation
- Ensure ETL/ELT processes and pipeline tools (Azure Data Factory, Databricks/Spark) run efficiently to deliver timely, high-quality data for analytics, BI, and AI/ML.
- Design and implement automation to significantly reduce recurring DBA and operational tasks, minimizing manual intervention.
- Develop monitoring, alerting, and self-healing mechanisms to proactively maintain platform health and SLA adherence.
- Identify and resolve bottlenecks, continuously tuning for performance and scalability.
Domain-Specific Solutions
- Design and implement algorithms supporting availability guarantees, contractual agreement calculations, regulatory reporting (e.g., GADS), and other domain-specific requirements.
- Serve as Subject Matter Expert (SME) for performance engineering — profiling, tuning, and resolving issues across database and pipeline layers.
AI & Agentic Capabilities Integration
- Incorporate AI and agentic capabilities as complementary components of the data platform.
- Design and evolve secure LLM integration patterns using enterprise LLM gateways to centralize model access, routing, governance, and cost controls.
- Leverage frameworks like Model Context Protocol (MCP) to connect AI applications and agents with enterprise data sources in a secure, governed manner — enabling intelligent, agent-driven data workflows.
Documentation, Collaboration & Enablement
- Partner with business stakeholders, product teams, and engineering groups to translate requirements into scalable data solutions.
- Create and maintain clear documentation for data architecture, integration processes, and platform best practices in collaboration with the Enterprise Architecture team.
- Provide technical leadership and mentorship within the technology team; establish best practices and participate in design reviews.
- Collaborate with third-party vendors and system integrators on data platform integrations and joint delivery initiatives.
Required Qualifications
- Bachelor’s degree in Computer Science, Data Engineering, Data Science, or a related field.
- 10+ years of experience designing and evolving large-scale data analytics platforms, with deep expertise in data integration (ETL/ELT), medallion-tier pipelines, cloud data services, and MLOps.
- Deep expertise in SQL — query optimization, schema design, indexing strategies, stored procedures, and performance tuning across platforms such as Microsoft SQL Server or Azure SQL.
- Hands-on experience with Microsoft Azure data services (Azure SQL, Azure Data Factory, Azure Synapse Analytics, Azure Data Lake, Blob Storage).
- Proven experience designing and building on Databricks, including Delta Lake, Spark jobs, and cluster management.
- Strong familiarity with data lakehouse architecture and applying data mesh principles across enterprise environments.
- Proven experience with: Azure Data Services (Synapse, Data Factory, Data Lake), Delta Lake, Azure Databricks.
- Solid understanding of enterprise data governance, security (access controls, data privacy), and data quality best practices.
- Demonstrated success automating DBA and data operations tasks to significantly reduce manual workload.
- Experience working with contractual or regulatory reporting requirements in data-intensive industries (e.g., energy, utilities, or finance).
- Strong communication and interpersonal skills; ability to work effectively with both technical and non-technical stakeholders across multiple concurrent priorities.
Preferred Qualifications
- Experience integrating AI/ML or LLM solutions into data platforms (e.g., Azure Cognitive Services, LLM gateways for multi-provider model integration, or context frameworks like MCP for AI-driven data products).
- Experience with energy sector data systems, including solar forecasting, GADS reporting, or availability guarantee frameworks.
- Experience with DevOps practices for data: CI/CD pipelines for database deployments, infrastructure-as-code (Terraform, Bicep, ARM templates).
- Knowledge of data security, encryption at rest/in transit, and RBAC in cloud environments.
- Microsoft Certified: Azure Data Engineer Associate or Databricks Certified Data Engineer Professional.
- Experience partnering with third-party data vendors and managing vendor-delivered integrations.
- Master’s degree in a relevant field.
Technical Skills Summary
Databases & SQL: SQL Server, Azure SQL, T-SQL, query optimization, indexing, stored procedures.
Cloud Platform: Azure Data Factory, Synapse Analytics, Data Lake, Blob Storage, Azure SQL.
Big Data / AI: Azure Databricks, Apache Spark, Delta Lake, AI/ML pipeline foundations, LLM integration.
Architecture Patterns: Medallion architecture, data mesh, data warehousing, ETL/ELT, dimensional modeling on Azure Databricks.
Automation & DevOps: Pipeline automation, CI/CD for data, scripting (Python, PowerShell, or equivalent).
Governance & Security: Data catalogs, RBAC, encryption, data quality, compliance frameworks.
Monitoring & Ops: Alerting, performance monitoring, incident mitigation, SLA management.
Working at NovaSource Power Services:
We value employee life outside of work and provide many ways to accommodate and support our staff in achieving their goals. You'll find a few ways we enact this below:
Experience comes in many forms, many skills are transferable, and passion goes a long way. If the job description gets you pumped but your background isn’t exactly what we’ve described above, or if you strongly believe you bring qualifications beyond what we’ve outlined that would help you excel in this position, please consider applying.
Potential candidates will meet the education and experience requirements provided on the above job description and excel in completing the listed responsibilities for this role.
US: Diversity Statement – Equal Employment Opportunity
It is NovaSource’s policy to provide equal employment opportunities to all applicants and employees. NovaSource disapproves of, and will not tolerate, unlawful discrimination against any applicant or employee because of race, color, national origin or ancestry, gender (including pregnancy, childbirth, or related medical conditions), gender identity, age, religion, disability, family care status, veteran status, marital status, sexual orientation, or any other basis protected by local, state or federal laws.
About NovaSource
NovaSource Power Services is the world’s #1-ranked solar operations and maintenance (O&M) provider and insight-driven total asset optimization partner for renewables asset owners ready to fuel smart growth. With over 20 years of operating experience and a presence on 5 continents, NovaSource has the global reach and strategic capabilities to achieve our clients’ renewables goals around the world.
NovaSource’s comprehensive approach to total asset optimization in addition to O&M services includes value engineering, performance analysis, strategic supply chain management, and advanced monitoring systems. The company operates in key global markets managing over 40GW of solar power plants. NovaSource’s expertise extends beyond solar and includes battery energy storage systems (BESS), offering a complete suite of services for the evolving renewable energy landscape.
Position Overview
We are seeking a hands-on Data & Analytics Platform Architect to serve as the technical authority for our enterprise data platform — designing, building, and continuously evolving the systems that power contractual, operational, analytical, and AI-driven workloads across the organization. This role combines strategic architecture with deep engineering ownership: you will lead the evolution of our Azure and Databricks-based data ecosystem, refine our multi-layer data pipelines, implement data mesh principles across multiple repositories, and drive high levels of automation to ensure a reliable, scalable, and cost-efficient platform. You will also explore and integrate emerging technologies — including AI/LLM capabilities — to enhance the platform’s intelligence and business value. Strong collaboration, commitment to incremental delivery, and the ability to mentor technical teams are essential.
This position follows a hybrid working schedule, with a combination of remote work and in-office collaboration.
Key Responsibilities
Enterprise Data Platform Architecture & Engineering
- Architect, build, and continuously improve the enterprise data platform, ensuring reliability, scalability, and maintainability across core business processes and analytics use cases.
- Own the full data platform lifecycle — from schema design and pipeline architecture to monitoring, performance tuning, and incident response.
- Establish and enforce data modeling standards, naming conventions, and governance frameworks across all environments.
- Implement policy enforcement points and access controls (data catalogs, encryption, RBAC) to ensure compliance, privacy, and data protection.
Analytical Data Modeling & Schema Design
- Design, build, and evolve dimensional data models — including star schemas on Azure Databricks — optimized for analytics and reporting.
- Develop and refine medallion architecture (bronze-silver-gold layers) for efficient data ingestion, transformation, and consumption.
- Balance model simplicity, flexibility, and performance while minimizing redundancy across analytical datasets.
Cloud & Big Data Architecture
- Lead the design and evolution of the Databricks intelligent data platform, enabling scalable big data processing and laying the foundation for AI/ML capabilities.
- Architect and manage Azure-based infrastructure including Azure SQL, Azure Data Factory, Azure Synapse Analytics, Data Lake, and related services.
- Apply data mesh principles across multiple data repositories to enable decentralized, domain-oriented data ownership.
Pipeline Optimization & Automation
- Ensure ETL/ELT processes and pipeline tools (Azure Data Factory, Databricks/Spark) run efficiently to deliver timely, high-quality data for analytics, BI, and AI/ML.
- Design and implement automation to significantly reduce recurring DBA and operational tasks, minimizing manual intervention.
- Develop monitoring, alerting, and self-healing mechanisms to proactively maintain platform health and SLA adherence.
- Identify and resolve bottlenecks, continuously tuning for performance and scalability.
Domain-Specific Solutions
- Design and implement algorithms supporting availability guarantees, contractual agreement calculations, regulatory reporting (e.g., GADS), and other domain-specific requirements.
- Serve as Subject Matter Expert (SME) for performance engineering — profiling, tuning, and resolving issues across database and pipeline layers.
AI & Agentic Capabilities Integration
- Incorporate AI and agentic capabilities as complementary components of the data platform.
- Design and evolve secure LLM integration patterns using enterprise LLM gateways to centralize model access, routing, governance, and cost controls.
- Leverage frameworks like Model Context Protocol (MCP) to connect AI applications and agents with enterprise data sources in a secure, governed manner — enabling intelligent, agent-driven data workflows.
Documentation, Collaboration & Enablement
- Partner with business stakeholders, product teams, and engineering groups to translate requirements into scalable data solutions.
- Create and maintain clear documentation for data architecture, integration processes, and platform best practices in collaboration with the Enterprise Architecture team.
- Provide technical leadership and mentorship within the technology team; establish best practices and participate in design reviews.
- Collaborate with third-party vendors and system integrators on data platform integrations and joint delivery initiatives.
Required Qualifications
- Bachelor’s degree in Computer Science, Data Engineering, Data Science, or a related field.
- 10+ years of experience designing and evolving large-scale data analytics platforms, with deep expertise in data integration (ETL/ELT), medallion-tier pipelines, cloud data services, and MLOps.
- Deep expertise in SQL — query optimization, schema design, indexing strategies, stored procedures, and performance tuning across platforms such as Microsoft SQL Server or Azure SQL.
- Hands-on experience with Microsoft Azure data services (Azure SQL, Azure Data Factory, Azure Synapse Analytics, Azure Data Lake, Blob Storage).
- Proven experience designing and building on Databricks, including Delta Lake, Spark jobs, and cluster management.
- Strong familiarity with data lakehouse architecture and applying data mesh principles across enterprise environments.
- Proven experience with: Azure Data Services (Synapse, Data Factory, Data Lake), Delta Lake, Azure Databricks.
- Solid understanding of enterprise data governance, security (access controls, data privacy), and data quality best practices.
- Demonstrated success automating DBA and data operations tasks to significantly reduce manual workload.
- Experience working with contractual or regulatory reporting requirements in data-intensive industries (e.g., energy, utilities, or finance).
- Strong communication and interpersonal skills; ability to work effectively with both technical and non-technical stakeholders across multiple concurrent priorities.
Preferred Qualifications
- Experience integrating AI/ML or LLM solutions into data platforms (e.g., Azure Cognitive Services, LLM gateways for multi-provider model integration, or context frameworks like MCP for AI-driven data products).
- Experience with energy sector data systems, including solar forecasting, GADS reporting, or availability guarantee frameworks.
- Experience with DevOps practices for data: CI/CD pipelines for database deployments, infrastructure-as-code (Terraform, Bicep, ARM templates).
- Knowledge of data security, encryption at rest/in transit, and RBAC in cloud environments.
- Microsoft Certified: Azure Data Engineer Associate or Databricks Certified Data Engineer Professional.
- Experience partnering with third-party data vendors and managing vendor-delivered integrations.
- Master’s degree in a relevant field.
Technical Skills Summary
Databases & SQL: SQL Server, Azure SQL, T-SQL, query optimization, indexing, stored procedures.
Cloud Platform: Azure Data Factory, Synapse Analytics, Data Lake, Blob Storage, Azure SQL.
Big Data / AI: Azure Databricks, Apache Spark, Delta Lake, AI/ML pipeline foundations, LLM integration.
Architecture Patterns: Medallion architecture, data mesh, data warehousing, ETL/ELT, dimensional modeling on Azure Databricks.
Automation & DevOps: Pipeline automation, CI/CD for data, scripting (Python, PowerShell, or equivalent).
Governance & Security: Data catalogs, RBAC, encryption, data quality, compliance frameworks.
Monitoring & Ops: Alerting, performance monitoring, incident mitigation, SLA management.
Working at NovaSource Power Services:
We value employee life outside of work and provide many ways to accommodate and support our staff in achieving their goals. You'll find a few ways we enact this below:
Experience comes in many forms, many skills are transferable, and passion goes a long way. If the job description gets you pumped but your background isn’t exactly what we’ve described above, or if you strongly believe you bring qualifications beyond what we’ve outlined that would help you excel in this position, please consider applying.
Potential candidates will meet the education and experience requirements provided on the above job description and excel in completing the listed responsibilities for this role.
US: Diversity Statement – Equal Employment Opportunity
It is NovaSource’s policy to provide equal employment opportunities to all applicants and employees. NovaSource disapproves of, and will not tolerate, unlawful discrimination against any applicant or employee because of race, color, national origin or ancestry, gender (including pregnancy, childbirth, or related medical conditions), gender identity, age, religion, disability, family care status, veteran status, marital status, sexual orientation, or any other basis protected by local, state or federal laws.
See All 159+ Platform Architect Jobs
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Find Platform Architect JobsPlatform Architect Job Market
A snapshot from current openings nationwide, updated as new roles post.
Who's Hiring
- Google22
- Boston Consulting10
- NVIDIA7
- Tata Consultancy Services (TCS)7
- Veeva Systems6
Top Industries Hiring
- Technology & Software54
- Consulting & Professional Services20
- Insurance16
- Electronics & Hardware13
- Banking & Financial Services12
What Employers Look For
The qualifications that appear most often in platform architect jobs.
- Bachelor's degree in computer science, software engineering, or a related technical field
- 5 or more years of experience designing and owning large-scale distributed systems or cloud platforms
- Deep expertise in at least one major cloud provider such as AWS, Azure, or Google Cloud Platform
- Proficiency with microservices, API gateway patterns, event-driven architecture, and containerization using Kubernetes or Docker
- Experience leading cross-functional technical alignment across multiple engineering teams or business units
- Cloud architecture certifications such as AWS Solutions Architect Professional or Google Cloud Professional Cloud Architect
Tips for Your Platform Architect Job Search
Tailor your resume to platform scope
Platform architect roles vary widely in scale. Hiring managers want to see the size of the platforms you've owned, the number of services or teams depending on them, and how your architectural decisions affected reliability or delivery speed.
Document your architecture decision records
Many hiring panels ask you to walk through a past architectural decision end to end. Prepare two or three documented examples covering the problem, options considered, tradeoffs weighed, and outcome, so you can speak to them concretely rather than from memory.
Apply early to roles that fit
Migrate Mate lists platform architect openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Target postings that name your cloud stack
Platform architect job descriptions often list AWS, Azure, or GCP as hard requirements, not preferences. Filter for postings that match the cloud provider where you have the deepest experience so your resume clears the initial technical screen.
Prepare for system design rounds early
Most platform architect interviews include at least one whiteboard or diagramming session on distributed systems design. Practice designing for failure modes, data consistency, and horizontal scaling out loud, because interviewers evaluate your reasoning process as much as the final diagram.
Negotiate scope before you negotiate compensation
Before accepting an offer, clarify whether the role has authority to enforce architectural standards or only advisory influence. A title with no cross-team mandate can limit your impact and your career progression, so confirm reporting structure and decision rights during the offer stage.
Platform Architect Jobs: Frequently Asked Questions
Which companies are hiring the most platform architects?
The companies hiring the most platform architects right now include Google, Boston Consulting, and NVIDIA, with the largest share of openings in California, Texas, and Illinois, based on current listings on Migrate Mate as of June 2026. Demand is strongest at organizations undergoing cloud migration or consolidating multiple legacy systems onto a unified platform.
How many platform architect jobs are remote?
About 44% of platform architect openings are fully remote or hybrid as of June 2026, making it one of the more flexible senior technical roles available. Cloud platform and API-focused positions tend to be the most remote-friendly, while roles tied to on-premises infrastructure modernization or regulated industries more often require on-site presence.
How do you become a platform architect?
Start by building several years of hands-on software engineering or infrastructure experience, then move into technical lead or senior engineer roles where you own system design decisions. Develop expertise in distributed systems, cloud platforms, and API design patterns. Earn a relevant cloud certification to validate your skills formally, and progressively take on cross-team architectural responsibilities to demonstrate the scope and influence the role requires.
Can you get a platform architect job with little experience?
Breaking in with limited experience requires demonstrating architectural thinking even if your title hasn't reflected it yet. Contribute to open-source infrastructure projects, publish architecture decision records or technical blog posts, and pursue a cloud certifications to create a concrete portfolio. Targeting smaller companies or startups where senior engineers are expected to wear architectural hats can also give you the hands-on scope you need to build credibility.
What does the platform architect interview process look like?
The process typically starts with a recruiter screen followed by a technical phone interview covering distributed systems concepts and your experience with cloud platforms. You'll then move to a system design round where you diagram and defend an architecture for a real-world scenario, and often a cross-functional panel assessing how you align engineering teams on standards. Final rounds often include a presentation of a past architectural project you led.
Where can I find and apply to platform architect jobs?
You can find and apply to platform architect jobs on Migrate Mate, which lists current openings from across the United States. Search the available roles, find the ones that match your experience and target stack, and apply directly to each listing from the same page.
See All 159+ Platform Architect Jobs
Jump back to the full list of openings and apply to any platform architect role that fits.
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