Data Architect

About Us Zapcom is a global Product Engineering and Technology Services company, specializing in bespoke, customer-centric solutions across industries like BFSI, e-commerce, retail, travel, transportation, and hospitality. Headquartered in the US, with a presence in India, Europe, Canada, and MENA, we excel in transforming ideas into tangible outcomes using AI, ML, Cloud solutions, and full-stack development. At Zapcom, we value accountability, ownership, and equality, empowering you to excel. We listen to your aspirations and provide the support needed to achieve them. Our diverse, collaborative culture ensures every voice is heard, driving innovation and business value. With global opportunities and expansion plans, now is the perfect time to join our team. Work on impactful projects that shape the future. Apply today and be part of something extraordinary! • Lead the assessment and redesign of our enterprise data architecture. • Deliver a current-state review and future-state design in the first 8 weeks. • Balance immediate reporting needs with long-term modernization by designing an interim architecture. • Establish an Azure-first data platform covering ingestion, storage, governance, analytics, and AI/ML workloads. • Work with business leaders to translate initiatives into project charters, ensuring quick wins as well as longer-term priorities. • Build and maintain a roadmap that phases delivery of quick wins, foundational capabilities, and advanced use cases. • Drive data governance and quality practices into day-to-day operations. • Collaborate with technical and business teams to turn data into measurable business value. First 6–8 Weeks: Assessment & Design • Complete a review of current data architecture including sources, pipelines, reporting, and governance. • Define the target-state architecture including data lakehouse and governance design. • Develop an interim-state architecture to support ongoing reporting and analytics. • Translate business requests into clear project charters, focusing on quick wins. • Deliver a roadmap with phased delivery of near-term wins and longer-term capabilities. 8–24 Weeks: Operationalization & Quick Wins • Stand up core Azure platform components (Data Factory, ADLS Gen2, Databricks, Synapse, metadata catalog). • Roll out governance processes including data cataloging, stewardship roles, and quality measures. • Deliver short-term business solutions using interim reporting and data marts. • Turn chartered business initiatives into projects with clear outcomes. • Pilot leapfrog initiatives such as real-time analytics, AI/ML model pipelines, or self-service analytics. 6–12 Months: Scale & Transition • Expand the enterprise data lakehouse with additional curated domains. • Shift the majority of reporting and analytics from interim to target-state architecture. • Operationalize AI/ML pipelines at scale with continuous training and deployment. • Show clear improvement in data quality, governance compliance, and speed of insight. Requirements • Strong background in enterprise data architecture, including data lakehouse design. • Hands-on knowledge of the Azure ecosystem: Data Factory, Event Hubs, ADLS Gen2, Data bricks, Synapse Analytics, Purview, Cosmos DB, Power BI, Azure ML. • Ability to translate business needs into technical solutions and formal project charters. • Experience balancing short-term requirements with future-state modernization. • Knowledge of data governance frameworks (e.g., DAMA, DCAM), metadata management, and stewardship practices. • Excellent stakeholder management and communication skills; able to bridge executives, business teams, and engineers. • Experience leading and mentoring data engineers and analysts. • 8+ years of experience in data architecture/engineering with at least 3 years working on Azure platforms. • Preferred: Microsoft Azure certifications (Data Engineer Associate, Solutions Architect Expert). Success Measures First 6-8 Weeks • Current-state assessment and target-state design complete. • Interim architecture to support critical reporting. • Business initiatives translated into project charters. • Roadmap published. 8–24 Weeks • Foundational Azure data platform operational. • Governance processes launched. • Quick-win projects delivered. • Leapfrog pilots underway. 6–12 Months • Lakehouse expanded with curated domains. • Majority of reporting transitioned to target-state. • AI/ML pipelines in production. • Noticeable improvements in data quality and time-to-insight.