Data & Analytics Platform Architect

Job Description

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.

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.