Jobs at Xebia

Designs and maintains enterprise semantic models and analytical layers using Power BI, advanced DAX, SQL, and Microsoft Fabric. The role translates business KPIs into scalable BI solutions, optimizes performance, manages governance and security, and collaborates with data engineers and stakeholders. It also supports AI-driven analytics, Microsoft Copilot, conversational queries, and reusable models for digital sales and customer behavior insights.

Develop AI-powered applications, conversational analytics experiences, agents, and copilots using enterprise data. Build prototypes with Python, SQL, APIs, Azure, Microsoft Fabric, and Microsoft AI technologies, applying RAG, grounding, tool calling, and agent orchestration. Evaluate AI output quality, security, and accuracy; collaborate with data and analytics engineers; and transition successful prototypes into production while identifying practical GenAI opportunities.

Design and implement federated data governance within a Data Mesh architecture. Establish enterprise standards, ownership models, data contracts, metadata requirements, quality metrics, and lifecycle policies. Partner with engineering teams to implement Databricks governance capabilities, including Unity Catalog, access controls, auditing, and lineage. Lead governance onboarding, develop playbooks, coach domain stakeholders, track adoption and compliance KPIs, and ensure alignment with privacy, security, sustainability, and regulatory requirements.

Designs, builds, and maintains reliable, scalable, secure infrastructure services. Develops automation and tooling, manages cloud and Kubernetes environments, improves performance and resilience, monitors system health, and leads incident investigation and root cause analysis. The role contributes to observability, alerting, capacity planning, infrastructure architecture, continuous delivery, and lifecycle management while collaborating with application teams. Required expertise includes Linux, GCP or equivalent cloud platforms, Docker, Kubernetes, distributed systems, infrastructure automation, monitoring, cloud security, and incident management.

Develop and maintain automated and manual testing across multiple layers using TypeScript and Playwright. Build API, integration, and contract tests; collaborate with developers on unit and component testing, mocking, stubbing, and testability. Integrate tests with Azure DevOps pipelines, reporting, and quality gates. Manage test data, SQL diagnostics, and failure analysis while coaching teams on pragmatic quality, risk, coverage, and maintainability.

Leads a DevOps and Platform Engineering team while designing and operating shared, multi-account, multi-region AWS platforms. Responsibilities include Kubernetes and container platform development, CI/CD, Terraform infrastructure automation, networking, security, compliance, cost optimization, modernization, technical evaluations, migrations, mentoring, and cross-team collaboration. The role also promotes AI-assisted software delivery and standardized engineering practices.

Develop performance testing strategies, automation frameworks, and quality engineering practices across multiple test layers. Build and improve Playwright and TypeScript frameworks, automate API and integration tests, and support contract testing. Integrate tests with Azure DevOps pipelines, reporting, and quality gates. Collaborate with developers on unit testing, mocking, stubbing, testability, and failure diagnosis. Manage test data and SQL investigations while coaching teams on risk, coverage, and maintainability.

Provides first-line IT support for local users, troubleshooting computers, peripherals, basic network issues, and conference room equipment. Maintains office IT inventory, coordinates small equipment purchases, performs minor repairs, and monitors local infrastructure. The role also manages business travel bookings, expense documentation, and office administration while coordinating with internal teams and vendors in English. Requires onsite work in Wrocław.

Manage full-cycle IT recruitment for German and DACH-region clients, from sourcing and screening through client presentations, interviews, feedback, offers, and onboarding handover. Build talent pipelines across Data Engineering, Cloud/DevOps, Software Engineering, and Architecture. Coordinate with Sales, Account Management, Delivery, candidates, and clients while maintaining ATS records, sharing market intelligence, supporting employer branding, and ensuring GDPR-compliant recruitment processes.

Build, deploy, and maintain production machine learning services and tooling for advertising applications. Responsibilities include MLOps workflow orchestration, model packaging, deployment, monitoring, alerting, rollback strategies, lifecycle management, troubleshooting, and reliability improvements. The role applies DevOps and SRE practices, collaborates with Data Science and Research teams, and supports cloud-based ML workloads using Python, SQL, and GCP.

Designs domain data models, event-driven integrations, analytics architectures, AI data contexts, governance frameworks, and GDPR-compliant solutions. The role covers auditability, historical data, Kafka and CDC patterns, metrics, reconciliation, lineage, knowledge graphs, vector stores, privacy-sensitive datasets, and alignment between experimental and production engineering teams.

Build AI agent systems for credit, KYC, compliance, document processing, and exception triage. Develop model routing, permissioned MCP tools, self-hosted model support, Document AI, retrieval architectures, knowledge graphs, and evaluation frameworks. Ensure agent behavior is observable, auditable, and secure while collaborating across prototype and production engineering teams.

Design and develop scalable backend solutions using Golang and Python. Build reliable APIs, complex business workflows, and large-scale state machines while contributing to architecture and technical direction. Ensure code quality through automated and end-to-end testing, establish guardrails for AI-generated code, and work with cloud-native technologies. Collaborate with engineering teams, own technical challenges, and deliver reliable software solutions.

Design, build, and maintain scalable cloud platforms on GCP. Automate infrastructure and deployments with Terraform and GitOps, manage Kubernetes environments and platform services, support production operations, collaborate with development and experimental teams, and contribute to cloud-native architecture and reliability.

Build and maintain full-stack applications using React, Kotlin, and Python. Develop responsive and accessible single-page applications, APIs, integrations, and microservices. Apply software engineering best practices, automated testing, code review, CI/CD, cloud infrastructure, Infrastructure as Code, monitoring, and troubleshooting. Collaborate independently and with Product, UX, and engineering teams in Agile environments while contributing across frontend and backend technology areas.

Build and improve Playwright-based test automation using TypeScript across UI, API, integration, and component layers. Integrate automated testing with Azure DevOps pipelines, reporting, and quality gates. Collaborate with developers on testability, mocking, stubbing, unit testing, and code reviews. Manage test data and SQL, diagnose failures across systems and environments, and coach teams on pragmatic quality, coverage, maintainability, accessibility, performance, security, and AI-assisted testing practices.

Design and develop backend services, APIs, and full-stack solutions using Go and TypeScript. Build complex workflows and state machines, work with PostgreSQL, create automated end-to-end tests, and establish guardrails for AI-generated code. Contribute to architecture and technical decisions while delivering scalable, production-ready systems. Collaboration, English proficiency, EU-based work authorization, and a work permit are required.

Coordinate delivery of pharmaceutical R&D platforms and applications across engineering teams. Responsibilities include facilitating Agile ceremonies, tracking sprint progress and capacity, managing technical dependencies, risks, issues, blockers, project plans, Jira dashboards, and delivery reporting. The role coordinates SDLC activities across development, testing, deployment, and release, while aligning Product, Program, and Engineering stakeholders in a distributed environment.

Own and extend an existing machine-learning solution for first-order product recommendations. Build and productionize models, Azure Machine Learning training and inference pipelines, scheduled retraining, validation gates, monitoring, drift detection, and data-quality checks. Integrate outputs with data warehouses and Power BI, maintain reproducibility and versioning, support testing and rollout, and collaborate with AI/ML, BI, and business teams during a 15-week MVP.

Lead architecture and migration of data pipelines across Airflow, Spark, dbt, and BigQuery. Preserve pipeline outputs through regression testing, refactor duplicated logic, establish testing and incident-management practices, implement data-lifecycle verification, document architecture decisions, and mentor data engineers. The role requires hands-on technical leadership, pipeline migration experience, engineering-practice adoption, and collaboration across the team.