Software Engineer II - Data Catalog
Rippling · Bengaluru · 2+ yrs experience · Posted 2026-03-06
Tech stack: Elasticsearch, Go, Python, React, TypeScript
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About the role
About the Data Catalog Team The Data Catalog team is the semantic intelligence layer of Rippling's platform. Our mission is to make Rippling's data schema — spanning millions of fields across every product — discoverable, trustworthy, and actionable for both humans and AI. We are the authoritative source of truth for "data about data" within Rippling. We sit at the intersection of platform infrastructure and AI, powering core product surfaces like Workflow Automator, Reports, Supergroups, and Data Manager, while increasingly serving as the data backbone for Rippling's AI products — including the AI Assistant and AI Field Selection features. What We Build The team delivers across four strategic pillars: Data Discoverability: Catalog browse, search, and field-selection experiences used throughout Rippling, enabling users to find the most relevant data for their use case across a vast and growing schema. Data Enrichment: AI-generated semantic descriptions, glossary tagging, sampled values, usage signals, and RQL formula examples — adding business context that makes raw schema meaningful. Data Governance: PII classification, sensitive data flagging, compliance tagging, and integration with access controls and auditing infrastructure. Data Lifecycle: Upstream and downstream dependency tracking, lineage visualization, and event-driven metadata sync to ensure the Catalog stays live and accurate as Rippling's data model evolves. What You Will Need 2+ years of professional experience with a focus on Backend development, demonstrating the ability to deliver reliable, end-to-end products using a backend language (e.g., Python/Go). Demonstrated ability to deliver reliable, well-scoped projects end-to-end with a high standard of quality. Prioritize projects based on business impact, ensuring high-quality implementation without unnecessary complexity. Ability to thrive in dynamic environments, quickly switching context between frontend and backend work as needed to accelerate delivery Strong engineering judgment: ability to translate open-ended requirements into clean technical designs and make pragmatic trade-offs. Clear, concise communication of technical ideas — in design documents, code reviews, and cross-functional discussions. Growth mindset: you proactively seek feedback, act on it, and hold yourself accountable to continuous improvement. Bonus: Working experience on frontend components for your own projects on and frontend technologies like React/TypeScript Experience with search infrastructure (Elasticsearch, OpenSearch, or similar). Exposure to AI/ML-adjacent systems — LLM integrations, semantic embeddings, or metadata enrichment pipelines. Familiarity with data governance concepts: PII classification, data lineage, or access control systems.
Qualifications
- 2+ years of professional experience with a focus on Backend development, demonstrating the ability to deliver reliable, end-to-end products using a backend language (e.g., Python/Go).
- Demonstrated ability to deliver reliable, well-scoped projects end-to-end with a high standard of quality.
- Prioritize projects based on business impact, ensuring high-quality implementation without unnecessary complexity.
- Ability to thrive in dynamic environments, quickly switching context between frontend and backend work as needed to accelerate delivery
- Strong engineering judgment: ability to translate open-ended requirements into clean technical designs and make pragmatic trade-offs.
- Clear, concise communication of technical ideas — in design documents, code reviews, and cross-functional discussions.
- Growth mindset: you proactively seek feedback, act on it, and hold yourself accountable to continuous improvement.
- Bonus: Working experience on frontend components for your own projects on and frontend technologies like React/TypeScript
- Experience with search infrastructure (Elasticsearch, OpenSearch, or similar).
- Exposure to AI/ML-adjacent systems
- LLM integrations, semantic embeddings, or metadata enrichment pipelines.
- Familiarity with data governance concepts: PII classification, data lineage, or access control systems.
Responsibilities
- The team delivers across four strategic pillars: Data Discoverability: Catalog browse, search, and field-selection experiences used throughout Rippling, enabling users to find the most relevant data for their use case across a vast and growing schema.
- Data Enrichment: AI-generated semantic descriptions, glossary tagging, sampled values, usage signals, and RQL formula examples — adding business context that makes raw schema meaningful.
- Data Governance: PII classification, sensitive data flagging, compliance tagging, and integration with access controls and auditing infrastructure.
- Data Lifecycle: Upstream and downstream dependency tracking, lineage visualization, and event-driven metadata sync to ensure the Catalog stays live and accurate as Rippling's data model evolves.