Practice Lead - Data OS

About the Role As the Head - Data OS - Solution, you will lead the strategic vision, architecture, and solutioning for our flagship Data Operating System offerings. You will architect end-to-end solutions that serve as the intelligent "operating system" for enterprise data unifying ingestion, storage, processing, governance, observability, and data product deployment in a scalable, AI-native manner. Reporting to senior leadership, you will bridge technical excellence with business impact, driving adoption of our Data OS platform while managing cross-functional teams and key stakeholders. This role requires a rare combination of deep solution architecture expertise, strong data product mindset, exceptional storytelling abilities, and proven people & stakeholder leadership in complex data environments. Responsibilities Data Product & Solution Thinking: Define and evolve the Data OS as a platform for building, deploying, and managing domain-specific, reusable data products. Apply strong data product thinking to ensure solutions are outcome-oriented, adaptable, AI-ready, and deliver fast ROI with minimal rework. Solution Architecture Leadership: Own the end-to-end solution architecture for Data OS implementations, including microservices-based designs, integration with modern data stacks (e.g., lakehouses, cloud-native tools), data pipelines, governance layers, observability, and security/compliance frameworks. Technical Storytelling & Evangelism: Craft compelling narratives, presentations, demos, and proposals that translate complex Data OS concepts into clear business value for clients, executives, and internal teams. Excel at articulating how the Data OS unifies disparate tools, democratizes data, and enables AI innovation. People Management: Build, lead, and mentor a high-performing team of solution architects, data engineers, product thinkers, and delivery specialists. Foster a culture of innovation, collaboration, technical depth, and continuous learning. Stakeholder Management: Serve as the primary point of contact for external clients (enterprise buyers, CDOs, data leaders) and internal stakeholders (product, engineering, sales, delivery). Manage expectations, align on requirements, handle escalations, and drive consensus on solution roadmaps and implementations. Innovation & Platform Evolution: Identify emerging trends in data operating systems, lakehouse architectures, DataOps, and AI integration; influence product roadmap and contribute to thought leadership through blogs, webinars, and industry engagements. Delivery Oversight: Ensure high-quality, on-time solution deployments with focus on scalability, performance, cost-efficiency, and client success metrics. Requirements12+ years of progressive experience in data platforms, with at least 68 years in senior solution architecture, platform leadership, or head-level roles focused on modern data ecosystems. Deep expertise in data product thinking - proven success in designing reusable, outcome-driven data products and treating data as products with lifecycle management, discoverability, and value realization. Strong solution architect knowledge - hands-on experience architecting unified data platforms, Data OS-like layers, microservices architectures, data fabrics, lakehouses (e.g., Delta Lake, Iceberg), governance (catalogs, lineage, quality), and integration across ingestion, processing, storage, and analytics tools. Exceptional technical storytelling skills - ability to simplify complex architectures into persuasive narratives, create impactful demos/PoCs, and influence C-level decisions through clear, business-aligned communication. Proven people management experience - successfully building and scaling high-performing technical teams, mentoring leaders, and driving performance in dynamic, innovative environments. Excellent stakeholder management - track record of navigating complex client and internal relationships, managing multi-stakeholder projects, and delivering business objectives in enterprise settings. Familiarity with leading modern data technologies (e.g., Snowflake, Databricks, cloud-native services, Kafka, dbt, Airflow, Unity Catalog equivalents) and understanding of DataOps principles. Strong business acumen, problem-solving mindset, and ability to thrive in a fast-paced, client-focused culture. BenefitsCompetitive compensation with performance-based incentives and strong growth upside .