We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at scale — across all devices and digital mediums, and our people exist everywhere in the world (15000+ experts across 26 countries, to be exact). Our work culture is dynamic and non-hierarchical. We are looking for great new colleagues. That is where you come in!
Job DescriptionKey Responsibilities
- Define and maintain the overall technical architecture for the KM data platform, spanning ingestion, storage (Databricks/Unity Catalog), transformation, security, and consumption layers.
- Translate multi-year program roadmaps into phased architectural plans, balancing near-term pilot needs against long-term scalability.
- Set architectural standards and guardrails for data modeling, pipeline design, and catalog registration, in partnership with the Senior Data Modeler and Data Engineers.
- Evaluate and recommend platform components, tools, and integration patterns (e.g., Unity Catalog vs. alternative catalogs, orchestration tools, vector search/embedding infrastructure).
- Lead technical design reviews and ensure alignment across data engineering, security/privacy, and downstream product teams (Knowledge Products, Research Products).
- Own non-functional requirements: scalability, performance, security, cost, and reliability of the platform architecture.
- Serve as the primary technical point of contact for architecture workshops and planning sessions (e.g., multi-day cross-functional architecture planning events).
- Assess technical risk and dependencies across workstreams and flag issues to the Engineering Manager and program leadership.
- Stay current on Databricks platform capabilities and enterprise AI/knowledge management architecture trends, bringing recommendations back to the team.
Required Qualifications
- 8+ years of experience in data/solution architecture roles, with significant hands-on or architectural experience in Databricks/Lakehouse environments.
- Deep understanding of Unity Catalog governance, data product design, and security classification models.
- Demonstrated experience architecting platforms that handle both structured and unstructured content at scale.
- Experience architecting for AI/LLM-driven consumption patterns (retrieval-augmented generation, vector search, agent-based data access) is highly valued.
- Strong track record of translating business/program roadmaps into technical architecture and staged delivery plans.
- Excellent stakeholder management skills — able to work across engineering, product, legal/privacy, and executive audiences.
- Experience leading architecture reviews and setting technical standards for a growing engineering team.
Preferred Qualifications
- Experience in professional services, consulting, or other knowledge-intensive industries.
- Familiarity with enterprise search/knowledge platforms (Glean, SharePoint, ServiceNow) and their integration patterns.
- Experience navigating legal/risk/privacy review processes for data platforms.
- Prior experience standing up a data platform team from a nascent or pilot stage to a scaled production capability. Success Metrics (First 6–12 Months)
- Documented target-state architecture for the KM data platform, validated with key stakeholders (Engineering Manager, Data Modeler, Product Owners).
- Architectural standards adopted across data modeling and engineering workstreams.
- Clear, staged technical roadmap aligned to the broader program timeline (e.g., through 2027).
Must have skills: Azure Data Factory, Data Modeling (Strong), Databricks
Skills Required
- 8+ years of experience in data or solution architecture roles
- Significant hands-on or architectural experience in Databricks or lakehouse environments
- Deep understanding of Unity Catalog governance
- Experience with data product design and security classification models
- Experience architecting platforms handling structured and unstructured content at scale
- Experience translating business or program roadmaps into technical architecture and staged delivery plans
- Strong stakeholder management across engineering, product, legal, privacy, and executive audiences
- Experience leading architecture reviews and setting technical standards
- Azure Data Factory experience
- Strong data modeling skills
- Experience in professional services, consulting, or knowledge-intensive industries
- Familiarity with Glean, SharePoint, ServiceNow, and enterprise search or knowledge platform integrations
- Experience navigating legal, risk, or privacy reviews for data platforms
- Experience scaling a data platform team from pilot to production capability
Nagarro Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Nagarro and has not been reviewed or approved by Nagarro.
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Pay Growth & Progression — Compensation is at times described as competitive, with salary hikes and perks occurring on certain occasions. Better growth opportunities and compensation are also positioned as an advantage versus other service-based companies.
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Flexible Benefits — Work arrangements are framed around a “work-from-anywhere” mindset with flexitime and family-friendly working models. This flexibility appears to add meaningful value to the overall rewards package for many roles.
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Healthcare Strength — Medical, dental, and vision coverage are described as available for employees and dependents, alongside life insurance. Mental-health support is also included via an Employee Assistance Program (EAP).
Nagarro Insights
What We Do
Nagarro helps future-proof your business through a forward-thinking, fluidic, and CARING mindset. We excel at digital engineering and help our clients become human-centric, digital-first organizations, augmenting their ability to be responsive, efficient, intimate, creative, and sustainable. Today, we are 19,000 experts across 36 countries, forming a Nation of Nagarrians, ready to help our customers succeed.








