Atmosera - Solution Architect, Data
Requirements
• 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). • 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.
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.
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