Solutions Architect

Atmosera empowers businesses to redefine what's possible with modern technology and human expertise. Our exceptional experience across Applications, Data & AI, DevOps, Security, and the Microsoft Azure platform enables organizations to accelerate innovation, enhance security, and optimize operational agility. As a Microsoft Partner with seven specializations, GitHub AI Partner of the Year, a member of the GitHub Advisory Board, and a member of the prestigious Microsoft Intelligent Security Association (MISA), Atmosera expertly delivers cutting-edge, integrated solutions that deliver business value. We are seeking a highly consultative, client-facing Solution Architect specializing in A zure Data Platform to serve as a technical pre-sales leader within our Data practice. This role is centered on data architecture leadership, helping clients define and realize modern data platforms, integration patterns, data modeling standards, and governance foundations on Azure. AI is an important part of our offerings, and this role ensures the data architecture is designed to enable analytics and AI solutions responsibly at scale. This is a high-impact role where you will partner closely with sales teams to lead discovery, solution design, and deal shaping activities , guiding clients from early-stage conversations through proposal, estimation, and successful deal closure. You will bring deep expertise across the Azure Data Platform to design scalable, secure, and governed data solutions. You will translate complex technical concepts into clear business value—connecting data strategy, platform choices, operating model, and delivery approach. You will also help clients understand how strong data foundations accelerate successful analytics and AI adoption in production. Responsibilities Technical Pre-Sales Leadership & Data Discovery Lead client-facing discovery sessions to understand business drivers, domain context, data products/use cases, and platform constraints. Assess current-state data architecture and maturity across ingestion, integration, storage/compute, data quality, metadata, lineage, security, and governance. Identify opportunities to modernize data estates (cloud, lakehouse/warehouse, medallion, integration patterns) and improve time-to-value for analytics. Translate business challenges into data architecture solution options, including the data foundations required to enable AI/ML and GenAI initiatives. Solution Architecture & Scoping Architect end-to-end Azure data platform solutions, including: Modern data platforms (Microsoft Fabric, Synapse, Databricks, ADLS) Data integration and orchestration (ADF, Fabric pipelines), including patterns for batch, streaming, and CDC Data modeling (conceptual/logical/physical), semantic modeling, and BI enablement (Power BI) Security, governance, and platform operations (RBAC, networking, encryption, monitoring, cost management) Data quality, catalog/metadata, lineage, and master/reference data considerations Define how analytics and AI will consume and be governed by the data platform, including: AI/ML and GenAI enablement patterns (feature/serving, vector search, RAG data pipelines) using Azure Machine Learning and Azure OpenAI as needed Responsible AI and data controls (privacy, sensitivity labels, access patterns, auditability) Define solution scope, delivery approach, and assumptions. Develop Statements of Work (SOWs), proposals, and platform estimates , ensuring alignment to client goals, timelines, and budgets. Data Strategy, Governance & Enablement Guide clients on data strategy and target-state architecture, including data product thinking, operating model, and a pragmatic modernization roadmap. Define patterns for governance (policies, stewardship, catalog/metadata, lineage), security, privacy, and compliance across the data estate. Advise on use case prioritization across data platform modernization, analytics/BI, and AI/GenAI—anchored in data readiness and measurable outcomes. Demonstrate the “Art of the Possible” by showing how a strong data architecture accelerates AI adoption (e.g., trusted datasets, governed access, RAG-ready knowledge, and monitoring-ready pipelines). Microsoft Co-Sell & Go-To-Market Support Partner with Microsoft field teams as the technical lead for Azure Data Platform pursuits, with AI/analytics included as part of the broader solution when applicable. Deliver technical presentations, architecture workshops, and demonstrations focused on data platform modernization and governed analytics, with AI examples as needed. Align solutions to Microsoft strategic priorities, including Fabric and Azure data services; incorporate Azure OpenAI/Azure AI capabilities when they materially support client outcomes. Position clients for funding programs and incentives tied to modernization and innovation. Proposal Development & Deal Shaping Own the technical components of proposals, including: Data architecture diagrams (ingestion/integration, lakehouse/warehouse, governance/security, and consumption) Solution narratives (business + technical) that clearly articulate data value and a delivery approach When required, an AI enablement layer (e.g., RAG-ready data pipelines and governed access to models) Shape deals that balance feasibility, scalability, security, and time-to-value. Ensure solutions are structured for incremental value delivery (assessment → foundation → migration/modernization → optimization), with AI delivered only after the data foundation is production-ready. Client & Internal Enablement Lead workshops on data platform modernization, data modeling, governance, and analytics enablement. Provide architectural guidance to delivery teams, ensuring continuity from pre-sales through implementation. Contribute to reusable assets, including data architecture patterns, accelerators, governance templates, and reference architectures . Required Experience & Skills Core Technical Expertise Deep experience designing Azure Data Platform architectures, including: ADLS, Microsoft Fabric, Synapse, Databricks Ingestion/integration patterns (batch, streaming, CDC), transformation, and orchestration Data modeling (dimensional, Data Vault, and domain-aligned approaches) and semantic models Governance and platform operations (catalog/metadata, lineage, data quality, security, monitoring, cost controls) Strong experience defining data architecture standards (reference architectures, patterns, decision records) and guiding teams to implement them consistently. Working knowledge of AI/ML and Generative AI as consumers of the data platform. Architecture & Design Proven ability to design scalable, secure, and production-grade data architectures (reliability, performance, cost, and operability) across enterprise workloads. Strong grasp of data governance, privacy, and compliance considerations, including how they affect analytics and AI consumption. Understanding of how modern data platforms enable analytics and AI/GenAI delivery (data products, trusted datasets, monitoring-ready pipelines). Consultative & Pre-Sales Skills Strong experience in pre-sales solutioning , including discovery, qualification, and deal shaping. Proven ability to create proposals, SOWs, and cost estimates for complex Data & AI engagements. Ability to clearly articulate business value of data, AI, and agentic systems , including ROI and operational impact. Skilled in leading executive-level discussions, workshops, and whiteboarding sessions . Collaboration & Communication Experience working in Microsoft co-sell environments , particularly around Azure data platform modernization (with analytics/AI as applicable). Strong collaboration across sales, delivery, and engineering teams. Excellent storytelling and presentation skills, especially around data modernization and governed analytics transformation journeys. Preferred Certifications AZ-305: Designing Microsoft Azure Solutions DP-203: Azure Data Engineer Associate Microsoft Fabric / DP-600 (preferred) AI-102: Azure AI Engineer Associate (nice to have) Azure AI Foundry / GenAI-related certifications (emerging, optional) This is a contractor position with the ability to work from home but may require travel to a client site. Atmosera is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. All employment is decided on the basis of qualifications, merit, and business need. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.