Jobs at Xebia

Owns the development, validation, and continuous improvement of AI-powered analytics solutions, including Talk2Data and agent-ready data. Defines analytical requirements, evaluation metrics, quality frameworks, and data models; validates large datasets and AI outputs; collaborates with data engineers and business stakeholders; supports governance, deployment, enablement, and proof-of-concept initiatives; and evaluates AI tools and integrations.

Own financial controlling for service lines, including management reporting, budgeting, forecasting, variance analysis, profitability, utilization, margin, cash flow, and working capital reporting. Build Excel and BI dashboards, support month-end close, track KPIs, improve reporting processes, and help implement financial systems. The role partners with Accounting, regional and Group leadership, and business teams while delivering actionable analyses and process improvements.

Develop and maintain high-availability frontend solutions using React, TypeScript, JavaScript, and microservices. Design new projects, improve customer experience and platform architecture, apply data-driven experimentation and A/B testing, and troubleshoot performance and browser compatibility issues. Mentor developers, strengthen coding and testing practices, and collaborate with product teams, stakeholders, and external partners. The role also involves using AI-powered development assistants and contributing to CI/CD and cloud-based delivery.

Design and develop an AI-powered analytics agent in Devin, integrating GitHub, dbt, Databricks, Snowflake, and data ingestion systems. Adapt dbt workloads for Databricks, resolve platform compatibility issues, build and test agent capabilities, and implement guardrails, validation, observability, security, and reliable AI-assisted workflows. Collaborate with client engineering teams, troubleshoot integrations, document the solution, and prepare knowledge-transfer materials.

Validate migrated and reporting data across source and target systems, with emphasis on SQL-based reconciliation, data quality checks, ETL/ELT validation, discrepancy analysis, and root-cause investigation. Define test scenarios, validate dashboards and KPIs, document defects, and collaborate with Data Engineers and stakeholders. The role also involves testing structured and semi-structured data, profiling datasets, using Python for automation, and supporting modern cloud data-platform delivery.

Designs and implements automation for incidents, service requests, and operational workflows. Builds Infrastructure-as-Code and deployment automation, improves monitoring and alerting, contributes to self-healing and auto-remediation, and applies software engineering practices. Collaborates with Platform Operations, SRE, and Service Management teams while developing reusable automation frameworks and mentoring engineers. Requires cloud, Kubernetes, CI/CD, observability, Python, and AI-assisted development experience.

Design and build responsive native iOS applications using SwiftUI and UIKit. Implement secure OAuth/OIDC authentication, token exchanges, networking, and integrations with external web services. Optimize performance, stability, memory usage, and quality while developing unit and end-to-end tests. Participate in code reviews, TDD, App Store releases, and modern AI-assisted software delivery.

Serve as the senior technical authority for Salesforce AMS, leading architecture, governance, L3 escalations, root-cause analysis, technical-debt remediation, code reviews, integrations, and scalable solution design. Provide technical leadership across Apex, Flow, LWC/Aura, APIs, and Salesforce security. The role requires Salesforce architect-level expertise, AI assistant experience, strong governance and support skills, Italian and English proficiency, and availability to work from the Milan office two days weekly.

Lead the end-to-end design, development, deployment, and maintenance of production AI and machine learning solutions. Define architecture standards, technical strategy, MLOps practices, governance, responsible AI controls, and AI Act compliance. Build and monitor models, advise stakeholders, troubleshoot production systems, and mentor engineers. The role also supports Generative AI, LLM applications, document processing, RAG, and scalable cloud-based ML platforms.

Designs, builds, evaluates, deploys, monitors, and improves machine learning and generative AI solutions. Responsibilities include data preparation, feature engineering, model lifecycle ownership, production troubleshooting, CI/CD, observability, model governance, responsible AI, and collaboration with engineers, architects, and business stakeholders.

Provides L2/L3 Salesforce support by investigating incidents, troubleshooting complex issues, performing root-cause analysis, recovering batch failures, implementing Apex, Flow, and LWC changes, and supporting releases and deployments. Maintains technical documentation, assesses managed-package updates, collaborates with technical and business teams, and improves Salesforce support processes.

Develop and maintain Salesforce solutions using Apex, SOQL, Flow, LWC, Aura, Platform Events, and Bulk API. Contribute to solution design, refinement, estimation, testing, documentation, quality gates, CAB submissions, deployments, and release support. Investigate defects through UAT and hypercare while collaborating with Agile teams across the software delivery lifecycle. The role requires Salesforce Platform Developer II certification, fluent Italian, EU work authorization, English communication skills, and practical use of AI-powered development assistants.

Lead discovery workshops and design complex user journeys from initial requirements through implementation. Create prototypes at varying fidelity, work within mature design systems, use UX metrics and data to validate decisions, and collaborate with product, analytics, engineering, and business stakeholders. The role requires confidently presenting design solutions, navigating complex organizational environments, and applying AI assistants to improve design and delivery workflows.

Leads end-to-end data transformation engagements, aligning business and technology stakeholders on data platform adoption, governance, ownership, quality, operating models, and handover. Validates organizational readiness, coordinates architects and engineering teams, translates technical findings into business decisions, and manages risks, issues, escalations, and delivery governance. Requires extensive data transformation leadership, executive stakeholder management, and experience with data platforms, governance, stewardship, quality remediation, lineage, and platform adoption.

Develop and maintain Java backend applications and REST APIs, migrate Java 8 systems to newer versions including Java 21/25, and design services using Spring and jOOQ. Work with REST-based microservices, Kafka, asynchronous communication, CI/CD, code reviews, and quality improvement. Collaborate with the team to deliver new functionality and improve existing systems. The role requires EU-based work authorization and involves a structured CV, HR, technical, and client interview process.

Build and improve an AI-powered accounting platform across frontend and backend. Responsibilities include developing React and Next.js experiences, Node.js services, REST APIs, PostgreSQL data models, integrations, and reliable AI workflows using LLM APIs, tool calling, streaming, and agents. The role owns features end to end, improves performance and reliability, makes architectural decisions, collaborates with product and domain experts, and uses AI development tools to deliver and maintain production software.

Design, build, and maintain scalable batch and real-time data pipelines, ELT workflows, cloud data infrastructure, orchestration, and streaming solutions. Ensure production reliability, performance, security, governance, monitoring, and data quality while collaborating with technical and business stakeholders. Participate in on-call support and continuous improvement initiatives.

Designs, implements, and operates secure, scalable Azure platforms and hybrid networking infrastructure. Builds Terraform-based Infrastructure as Code, CI/CD, GitOps workflows, automation, observability, governance, and developer self-service capabilities. Applies the Azure Well-Architected Framework and Generative AI to infrastructure development and troubleshooting. Collaborates with technical teams, mentors engineers, and promotes platform engineering practices in Agile environments.

Leads Discovery and business analysis for a complex IT initiative, defining analytical approaches, facilitating stakeholder workshops, eliciting and structuring requirements, modeling processes and user flows, identifying risks and dependencies, and shaping scope, roadmaps, estimates, and solution directions. The role coordinates business, UX, architecture, and engineering stakeholders, communicates recommendations, and uses AI-assisted tools to improve analysis and documentation.

Design, build, and maintain scalable data pipelines for BigQuery migrations. Develop ingestion and transformation frameworks, orchestrate and optimize workloads, ensure data quality, and deliver production-grade data products for reporting and analytics. Collaborate with analytics engineers, architects, and stakeholders on platform migration and sustainability. Required capabilities include Python, SQL, BigQuery, ETL/ELT, data modeling, orchestration, Git, CI/CD, cloud-native architectures, testing, monitoring, and data validation.