Sr Software Engineer at Sinch

, , Spain -

Full Time

Start Date

Immediate

Expiry Date

14 Oct, 26

Salary

80000.0

Posted On

16 Jul, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Go, Kafka, DynamoDB, AWS, Kubernetes, Distributed Systems, Microservices, Event-Driven Architecture, NoSQL, Data Modelling, CI/CD, GitOps, Docker, API Design, CQRS, Event Sourcing

Industry

IT Services and IT Consulting

Description

ABOUT SINCH Sinch is pioneering the way the world communicates. More than 150,000 businesses — including Google, Uber, Paypal, Visa, Tinder, and many others — rely on Sinch's Customer Communications Cloud to power engaging customer experiences through mobile messaging, voice, and email. Whether you need to verify users or craft omnichannel campaigns, Sinch makes it easy. Our AI-infused Super Network, APIs, and applications ensure you can connect with your customers reliably and securely, at every step of their journey. At Sinch we "Dream Big", "Win Together", "Keep it simple", and "Make it Happen". These values are our foundation! DESCRIPTION We are building the next generation of conversational AI infrastructure. As a Senior Software Engineer on the Context Storage team, you will design, build, and own the core data platform that captures, persists, and serves conversational context to AI agents operating across SMS, WhatsApp, and voice at scale. This is a zero to one opportunity to build a new platform from the ground up, working closely with all the teams belonging to the AI Agents domain. You will work on complex problems with high autonomy as part of a small senior team with significant impact. You will collaborate with Engineering Managers, Tech Leads, Product Managers, Domain Architects and cross-functional teams. Tech stack: Go, Kafka, DynamoDB, AWS, Kubernetes You will: * Design and build the event-driven pipelines that capture conversational and interaction events across channels and persist them into durable, queryable stores * Own the data model and storage architecture that represents conversational context in DynamoDB, designed for fast, consistent, and cost-efficient access by AI agents * Design system architecture and create technical proposals covering event ingestion, schema/table design, single-table vs. multi-table strategies, and read/write access patterns on DynamoDB * Build event sourcing / CQRS-style systems that reconcile high-throughput event streams via Kafka with strongly consistent context reads * Write production-grade Go services, including clean, maintainable code with tests and documentation; build systems that are observable, debuggable, and resilient * Own the full lifecycle of the systems you build: design, deployment, monitoring, and on-call responsibilities * Follow a build-ship-run approach * Design APIs and data contracts that let AI agent platform teams retrieve relevant context efficiently, addressing challenges like data freshness, TTL/retention, partition key design, hot partitions, and query latency at scale * Optimise DynamoDB capacity modes, indexing (GSIs/LSIs), and access patterns for cost, throughput, and latency as event volume and agent concurrency grow * Contribute to engineering excellence through architecture discussions, code reviews, and standards definition * Mentor team members and share knowledge * Collaborate across teams with product and technical stakeholders, providing technical guidance on trade-offs between consistency, availability, and performance REQUIREMENTS You should have: * 5–8+ years of professional software engineering experience * Strong backend and platform skills including distributed systems, microservices, event-driven architectures, and cloud-native design on AWS * Experience with Kafka or other event-streaming platforms * Deep expertise in Go (Golang) for building backend services; willingness to learn other languages/technologies as needed (Java, React) * Solid experience with data modelling and persistence at scale, ideally with DynamoDB specifically — partition/sort key design, GSIs/LSIs, capacity planning, and consistency trade-offs — or equivalent NoSQL experience * Ability to explain architectural trade-offs clearly, especially around storage, consistency, and throughput * Strong engineering fundamentals including clean code, testing strategies, debugging production systems, and building reliable, long-lasting systems * Experience using AI coding tools such as Copilot, Claude Code, Cursor, or similar, with an understanding of their benefits, risks, and best practices * Ability to work autonomously, define scope, and deliver solutions independently; strong decision-making on prioritisation * Experience with AWS and operations including core AWS services (DynamoDB, EKS, etc.), Docker, Kubernetes, CI/CD, GitOps, infrastructure as code, and monitoring * Strong communication skills with both technical and non-technical stakeholders * Fluent in English It's a plus if you also have: * Experience building platforms from scratch * Background in CPaaS or communication platform OUR HIRING PROCESS We are committed to ensuring a recruitment process that is fair, objective, consistent, and inclusive. Our approach includes structured, competency-based interviews designed to evaluate your skills, experience, and qualifications relevant to the role. At times, we may include a data-driven assessment to enhance our hiring success and identify candidates likely to excel. We believe in a two-way process and encourage you to ask questions throughout the journey. If this role isn't what you're looking for, please explore the other opportunities listed on our career page: https://www.sinch.com/careers/ [https://www.sinch.com/careers/]. No matter who you are, we hope you find an exciting path forward — hopefully with us! Salary The annual salary for this position in Spain is between €60,000 – €80,000. Factors which may affect pay within this range may include skills, education, experience, and other qualifications.

Responsibilities

Design and build a core data platform for capturing and serving conversational context to AI agents at scale. This includes developing event-driven pipelines, managing DynamoDB storage architecture, and ensuring system observability and resilience.