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Data Platform Architect Jobs (NOW HIRING)

MSR Cosmos

GCP Databricks Architect

Davidson, NC · On-site

$59 - $76/hr

Davidson, NC Onsite Duration: Long Term "BRIEF POSITION SUMMARY Experienced Data Platform Architect to design and manage enterprise grade data solutions This role will focus on building and ...

Sprouts Farmers Market

Platform Architect

Phoenix, AZ · On-site

This role owns the architecture and evolution of our data infrastructure across on-premises Microsoft SQL Server, Azure SQL, and open-source relational platforms including PostgreSQL, while ...

New

Prophecy Technologies

Platform Architect

Mclean, VA · On-site

Platform Architect - Salesforce Marketing Cloud Architect (with Data Cloud Experience Must) Location: MCLEAN VA We are seeking an experienced Salesforce Marketing Cloud (SFMC) Architect with strong ...

Flex Ltd.

Platform Architect - AI

Austin, TX · On-site

Job Summary Flex is seeking a Platform Architect to design and scale enterprise-grade platforms for deploying AI and data-driven applications across our global manufacturing and supply chain ...

Flex

Platform Architect - AI

Austin, TX

Job Summary Flex is seeking a Platform Architect to design and scale enterprise-grade platforms for deploying AI and data-driven applications across our global manufacturing and supply chain ...

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All JobsData Platform Architect Jobs

Data Platform Architect information

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How much do data platform architect jobs pay per hour?

As of Jun 14, 2026, the average hourly pay for data platform architect in the United States is $69.98, according to ZipRecruiter salary data. Most workers in this role earn between $61.30 and $78.85 per hour, depending on experience, location, and employer.

How much does a platform architect make?

A Data Platform Architect typically earns between $100,000 and $160,000 annually, depending on experience, location, and industry. Senior roles with specialized skills in cloud platforms and data management tools can command higher salaries, often exceeding $180,000.

How much money do data architects make?

Data architects typically earn a median annual salary between $100,000 and $150,000, depending on experience, location, and industry. Senior roles with specialized skills in cloud platforms and data modeling can exceed $160,000 annually.

What are the key skills and qualifications needed to thrive as a Data Platform Architect, and why are they important?

To thrive as a Data Platform Architect, you need deep expertise in data modeling, database design, cloud platforms, and related technologies, typically supported by a degree in computer science or a related field. Familiarity with tools like Azure, AWS, Google Cloud, ETL systems, and certifications such as AWS Certified Solutions Architect or Microsoft Certified: Azure Data Engineer are highly valued. Strong problem-solving, project management, and communication skills help you collaborate effectively with engineers and stakeholders. These competencies are vital for designing scalable, secure, and efficient data architectures that meet organizational needs.

What is a Data Platform Architect?

A Data Platform Architect is a professional responsible for designing and overseeing the development of a robust data infrastructure within an organization. They create scalable architectures that support data collection, storage, integration, and analysis, ensuring that data flows securely and efficiently across systems. This role requires expertise in cloud services, databases, data modeling, and data governance to help businesses make informed decisions based on reliable data. Data Platform Architects often collaborate with data engineers, analysts, and business leaders to deliver solutions that support analytics, reporting, and digital transformation initiatives.

What are some common challenges Data Platform Architects face when designing scalable data solutions?

Data Platform Architects often encounter challenges such as ensuring system scalability to handle rapidly growing data volumes, integrating disparate data sources, and maintaining data quality and security across platforms. Balancing the need for real-time data access with cost-effective storage and compute resources can also be demanding. Collaboration with data engineers, analysts, and business stakeholders is crucial to align technical solutions with organizational goals and to ensure the platform supports evolving business needs.

What does a data platform architect do?

A data platform architect designs and oversees the infrastructure that supports data collection, storage, processing, and analysis within an organization. They evaluate and implement technologies such as cloud services, data warehouses, and data pipelines, ensuring systems are scalable, secure, and efficient. Strong knowledge of data modeling, architecture principles, and tools like SQL, Hadoop, or Spark is essential for this role.

What is the difference between Data Platform Architect vs Data Engineer?

AspectData Platform ArchitectData Engineer
CredentialsTypically requires a bachelor’s degree in computer science, data management, or related fields; certifications like AWS Certified Data Analytics or Google Cloud Professional Data Engineer are common.Requires similar degrees and certifications, often including cloud platform certifications and programming skills.
Work EnvironmentFocuses on designing and overseeing data infrastructure, working closely with architects, stakeholders, and development teams.Builds, maintains, and optimizes data pipelines and databases, often working directly with data warehouses and ETL processes.
Industry UsageUsed across industries to develop scalable data platforms and strategies.Commonly employed in data integration, processing, and pipeline development roles.

While both roles require strong technical skills and knowledge of data systems, the Data Platform Architect primarily designs the overall data infrastructure, whereas the Data Engineer focuses on building and maintaining data pipelines. They often collaborate closely to ensure a robust data environment.

How much does a data architect get paid?

Data architects typically earn a salary ranging from $100,000 to $160,000 annually, depending on experience, location, and industry. Senior roles or those with specialized skills in cloud platforms and data modeling can command higher compensation.
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Data Platform Architect jobs near you
Infographic showing various Data Platform Architect job openings in the United States as of June 2026, with employment types broken down into 71% Full Time, and 29% Contract. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution, with an average salary of $145,556 per year, or $70 per hour.
Data & Analytics Platform Architect - Hybrid

Data & Analytics Platform Architect - Hybrid

NovaSource Power

Houston, TX • On-site

$60.75 - $78.25/hr

Full-time

Posted 24 days ago

Job description

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.