Overview:
Medallia is the pioneer and market leader in Experience Management. Our award-winning SaaS platform, Medallia Experience Cloud, leads the market in the management of experiences, insights, and actions for candidates, customers, employees, patients, and residents alike.
We believe that every experience is a memory that can last a lifetime. Experiences shape the way people feel about a company. And they greatly influence how likely people are to advocate, contribute, and stay. At Medallia, we are committed to creating a world where organizations are loved by their customers and their employees.
We empower exceptional people to create extraordinary experiences together.
Bring your whole self.
The Role
At Medallia, we help the world’s leading organizations capture experience signals and transform them into real-time intelligence and action. As AI-native systems evolve, traditional transactional architectures are no longer enough. The future belongs to platforms that can continuously sense, interpret, and react to live operational and behavioral signals in real time.
We are building the next generation of intelligent, event-driven platforms and are looking for a Principal Data & Event Intelligence Engineer to architect the real-time nervous system powering AI-driven decisioning, automation, and personalization across Medallia.
Mission
Transform the enterprise from static CRUD and transactional systems into a real-time intelligence platform. This role is responsible for building the foundational event and streaming infrastructure that powers operational AI, behavioral intelligence, autonomous workflows, and adaptive customer experiences. You will design the systems that continuously ingest, process, enrich, and activate enterprise signals at scale — including both operational (transactional) and analytical (OLAP) data paths, and the vector-based retrieval layers that ground AI systems — enabling AI systems and products to operate with real-time awareness and responsiveness.
Responsibilities:
Event-Driven Platform Architecture
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Architect and scale enterprise-wide event-driven systems powering real-time intelligence and AI workflows
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Design highly resilient streaming architectures supporting billions of events and low-latency processing, using Flink for stateful stream processing, windowing, and complex event processing
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Build foundational event ingestion, routing, enrichment, and distribution frameworks, including bidirectional streaming in and out of the data platform (source connectors, CDC pipelines, and downstream activation/egress into operational and analytical systems)
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Define event contracts, schemas, governance standards, and interoperability patterns across platforms
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Drive the transition from request/response transactional systems toward reactive, event-native architectures
Real-Time Signal & Behavioral Intelligence
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Build real-time customer, employee, product, and operational signal pipelines
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Develop behavioral intelligence systems that continuously analyze activity streams and contextual signals
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Create streaming analytics capabilities enabling dynamic decisioning, anomaly detection, and adaptive workflows
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Build operational AI feedback loops that continuously improve AI systems using live interaction data
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Enable AI systems to react to changing enterprise state with minimal latency
Data Infrastructure & Temporal Systems
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Design feature stores, event stores, and temporal data architectures optimized for AI and machine learning workloads
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Build systems supporting time-aware reasoning, sequence analysis, sessionization, and historical replay
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Architect low-latency operational data stores using YugabyteDB for distributed, horizontally scalable, strongly consistent transactional workloads
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Design and operate high-performance OLAP serving layers using StarRocks for real-time analytical queries over streaming and batch data at scale
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Design and evolve vector store architecture (indexing strategies, hybrid search, embedding lifecycle management) to support semantic search, RAG, and AI grounding use cases across the platform
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Implement cross-platform telemetry normalization and unified signal modeling
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Develop scalable infrastructure for high-throughput event storage, retention, replay, and observability
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Optimize data freshness, consistency, throughput, and processing efficiency at scale
Personalization & Agent Activation
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Build real-time personalization infrastructure powering adaptive user experiences and intelligent workflows
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Design agent-trigger systems enabling autonomous AI actions based on live signals and behavioral conditions
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Create context propagation and streaming enrichment systems supporting AI orchestration frameworks, including retrieval from vector stores for grounded, context-aware responses
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Partner with AI platform teams to integrate event intelligence into agentic and autonomous systems
Technical Leadership & Platform Strategy
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Establish architectural standards and best practices for streaming and event-driven engineering, and analytical/vector data infrastructure
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Drive platform modernization initiatives across data, infrastructure, and AI ecosystems
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Mentor engineers and influence technical direction across multiple teams and organizations
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Partner with Product, Infrastructure, AI, and Security teams to operationalize real-time intelligence capabilities
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Evaluate emerging technologies in streaming systems, event processing, OLAP engines, distributed databases, vector storage, and operational AI infrastructure.
Candidates based in the Buenos Aires vicinity will be prioritized as this role is Hybrid, 3 days per week onsite.
Qualifications:
Minimum Qualifications
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10+ years of experience building large-scale distributed systems, streaming platforms, or real-time data infrastructure with expertise in event-driven architectures and distributed streaming systems
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Demonstrated experience with technologies such as Kafka, Pulsar, Flink, Spark Streaming, Kinesis, or similar platforms
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Demonstrated experience building low-latency, high-throughput real-time processing systems at enterprise scale
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Demonstrated experience with distributed SQL / NoSQL databases such as YugabyteDB (or comparable distributed, strongly consistent databases like CockroachDB, Spanner) for transactional workloads at scale
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Demonstrated experience with OLAP/analytical engines such as StarRocks (or comparable systems like ClickHouse, Druid, Pinot) for real-time analytical serving
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Demonstrated experience or strong working knowledge of vector store design (e.g., Milvus, Pinecone, Weaviate, pgvector) including indexing, embedding storage, and retrieval architecture for AI/RAG use cases
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Demonstrated understanding of temporal data systems, feature stores, and streaming analytics architectures
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Demonstrated experience designing scalable telemetry, observability, or behavioral data platforms
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Demonstrated experience with programming in Java, Scala, Go, Python, or similar backend technologies
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Demonstrated experience with cloud-native infrastructure, Kubernetes, and distributed runtime environments
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Proven ability to lead large-scale technical initiatives across organizational boundaries
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Fluenct in English, oral and written
Preferred Qualifications
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Experience building operational AI or real-time personalization systems
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Familiarity with agentic systems, event-triggered workflows, or AI orchestration frameworks
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Experience integrating streaming systems with AI/ML pipelines and inference platforms
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Knowledge of real-time experimentation, recommendation systems, or adaptive decisioning architectures
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Experience with schema evolution, data governance, and multi-tenant event platform design
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Hands-on experience operating StarRocks or similar MPP OLAP systems in production at scale
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Hands-on experience operating YugabyteDB or similar distributed SQL databases in production, including sharding, replication, and multi-region topology design
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Experience designing hybrid retrieval systems combining vector similarity search with structured/OLAP filtering
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Contributions to open-source streaming, data infrastructure, or observability ecosystems
What Success Looks Like
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Build a scalable, resilient real-time intelligence platform powering AI-native experiences across Medallia
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Enable enterprise systems to continuously react to live customer and operational signals
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Reduce latency between signal generation, intelligence creation, and automated action
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Create foundational infrastructure for operational AI, autonomous workflows, and adaptive personalization
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Stand up a unified analytical and vector-retrieval layer (StarRocks + vector store) that powers both real-time BI and AI grounding use cases
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Standardize and modernize the company’s event-driven architecture strategy, including streaming ingress and egress patterns across the data platform
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Improve platform observability, telemetry quality, and behavioral intelligence across products and services
Why Join Medallia
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Help redefine enterprise architecture for the AI-native era
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Work on deeply technical distributed systems and real-time intelligence challenges at scale, spanning streaming, distributed transactional databases, OLAP, and vector retrieval
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Build the event infrastructure powering next-generation AI products and autonomous workflows
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Influence platform strategy across AI, data, and infrastructure engineering
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Collaborate with exceptional engineers and technical leaders solving complex enterprise-scale problems
At Medallia, we celebrate diversity and recognize the value it brings to our customers and employees. Medallia is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age (40 and over), disability, genetic information, veteran status or military service, or any other status protected by state or local law. Individuals with a disability who need an accommodation to apply please contact us at
[email protected]. For information regarding how Medallia collects and uses personal information, please review our Privacy Policies. Applications will be accepted for 30 days from the date this role was posted or until the role has been filled.
