Jobs at Xebia

Designs, develops, and maintains dbt-based transformation frameworks and analytical data models across Silver and Gold layers. Optimizes dbt execution, BigQuery-based transformations, cloud processing costs, and operational workflows. Manages dbt artifact versioning, Docker-based deployment, data quality, governance, and technical documentation while collaborating with Data Engineering, DevOps, and Platform teams.

Designs and maintains CI/CD pipelines, reusable delivery templates, deployment automation, artifact distribution, Docker workflows, and Kubernetes environments. Automates platform engineering activities, supports Python packaging migrations from Poetry or setuptools to UV, and improves software delivery governance, traceability, and repeatability. The role also collaborates with engineering teams on DevOps practices and maintains technical documentation, with opportunities to support GCP-based enterprise data platforms.

Design, configure, and enhance Google Knowledge Catalog solutions; build metadata integrations, enrichment, lineage, governance, access management, monitoring, and deployment automation. Conduct technical analyses and proofs of concept, document solutions, and support operational handover, readiness, and knowledge transfer while collaborating with governance and platform stakeholders.

Develop full-stack applications using Java, Spring Boot, Angular, REST APIs, PostgreSQL, and Domain-Driven Design. Integrate LLMs and Generative AI APIs to transform unstructured data into structured content and build AI-powered enterprise features. Collaborate in fast-paced PoC and MVP environments, adapt solutions to customer feedback, and use AI development assistants to improve delivery quality and productivity.

Design and implement scalable Databricks data pipelines, standardize and migrate legacy pipelines, develop dbt and Python transformations, onboard regional datasets, optimize performance and resource usage, and collaborate with architects and analytics teams. The role also contributes to future Data Science and MLOps foundations while supporting cloud-based data platforms and data modeling practices.

Develop production-grade TypeScript applications, backend services, APIs, system integrations, and agentic AI orchestration workflows. Integrate AI agents with external tools and existing systems, build cloud-based solutions using AWS, and contribute to reliable, scalable AI-powered applications. The role requires strong backend development, modern software practices, independent ownership, and practical use of AI coding assistants. The initial project is planned for two months, with potential extension.

Lead a squad of 8–20 engineers while remaining hands-on in frontend, backend, cloud, and AI development. Design domain-driven, event-driven architectures; build LLM-powered applications using Claude, AWS Bedrock, and LangGraph; translate Figma designs into accessible interfaces; establish engineering standards; manage CI/CD with GitHub Actions and AWS CDK; implement automated testing; and mentor engineers during agile delivery.

Lead discovery and rapid validation of an AI product initiative over a five-week freelance project. Responsibilities include customer interviews, prototype testing, hypothesis validation, requirements definition, prioritization, stakeholder communication, feasibility assessment, and go/no-go recommendations. The role works closely with software engineers, technical stakeholders, and 2–5 customer design partners to evaluate usability, business value, commercial potential, and integration readiness.

Design and build agentic AI platform capabilities, cloud-native enterprise applications, infrastructure, IaC, and CI/CD pipelines. Deploy and operate secure, governed solutions in regulated enterprise environments, integrate AI with legacy systems, and collaborate with architects and engineering leaders. The role requires strong software engineering, cloud engineering, agent platform development, technical leadership, and stakeholder management skills.

Implement and strengthen information security controls across CI/CD, APIs, endpoints, IAM, vulnerability management, and software supply chains. Build asset inventories and access matrices, integrate security scanning, enforce least privilege, manage secrets, support Qualys onboarding, coordinate remediation, document procedures, and collaborate with engineering, IT, and compliance teams. The role requires hands-on delivery, technical autonomy, strong English documentation, and knowledge of security frameworks and cloud environments.

Designs enterprise agentic AI architectures, evaluates platforms and buy-versus-build options, develops hands-on proofs of concept, and establishes secure, observable AgentOps/LLMOps lifecycles. The role requires integrating agents with APIs, MCP, and enterprise data, incorporating governance and AI risk controls, and converting prototypes into reusable production capabilities. The architect collaborates with senior stakeholders, provides decision-ready recommendations, and transfers knowledge through documentation, walkthroughs, and pairing.

Leads large, complex software programs for international clients with end-to-end accountability for delivery, budget, governance, risks, dependencies, change management, and stakeholder alignment. Manages distributed teams and external partners, mentors Delivery Managers, builds program teams, and ensures predictable delivery across scope, timelines, and contractual commitments. Requires initial work in India and frequent international travel thereafter.

Develop and maintain scalable PHP backend applications, databases, APIs, and cloud-based microservices. Responsibilities include implementing requirements, improving products, troubleshooting, testing, documentation, QA collaboration, and writing clean code. The role involves distributed multidisciplinary teamwork, mentoring, and knowledge sharing. Candidates need strong PHP 8+, MySQL, SQL, GraphQL, object-oriented programming, SOLID principles, design patterns, and unit and integration testing experience.

Design and develop modern Google Cloud data platforms and end-to-end data processing solutions. Define data and application architectures, integrations, data flows, disaster recovery, retention mechanisms, CI/CD, and infrastructure-as-code practices. Select and integrate GCP services, produce architectural documentation and diagrams, facilitate architecture workshops, and communicate technical decisions to business and technical stakeholders.

Manage and improve deployment, release, and infrastructure processes across development, lab, and production environments. Design reliable cloud and on-premises solutions, maintain Infrastructure as Code, support DevOps adoption, coordinate updates, troubleshoot critical production issues, and contribute to modernization, documentation, planning, and continuous improvement. The role requires AWS, Azure Pipelines, Docker, Terraform, Ansible, CI/CD, monitoring, firewall management, and experience with cloud and air-gapped environments.

Automate and maintain Linux environments, build and deployment pipelines, virtualized and air-gapped infrastructure, and product release processes. Troubleshoot Linux systems and Java applications, configure operating systems and applications, support builds across development and release environments, and maintain technical documentation. The role requires Bash, Git, Python, Ansible, virtualization, automated installation, and practical use of AI-powered development assistants.

Supports development teams through technical guidance, solution design, CI/CD maintenance, infrastructure automation, troubleshooting, monitoring, and observability. Manages AWS, on-premises, and air-gapped environments while improving configuration processes and delivery reliability. Collaborates with engineering teams on new features, technical planning, Agile practices, and continuous improvement. Requires hands-on AWS DevOps experience and practical knowledge of Terraform, Ansible, Bash, Git, Docker, CI/CD, AWS services, monitoring tools, and firewall management.

Manage AWS cloud infrastructure, distributed services, CI/CD pipelines, containers, monitoring, security, and integrations across technical teams. Troubleshoot infrastructure and application issues, maintain services including Redis, Elasticsearch, and IBM MQ, and provide technical guidance for solution and infrastructure design. The role requires strong AWS and DevOps expertise, AI-assisted software delivery experience, effective communication, and EU work authorization.

The Senior Cloud Security Engineer assesses cloud and on-premises security, coordinates penetration testing and security scanning, identifies and remediates risks, and establishes security standards. The role drives Security by Design, strengthens Azure and hybrid-cloud practices, integrates controls into CI/CD and DevSecOps workflows, supports security governance, and contributes to organizational security roadmaps. It also requires stakeholder communication, workshop facilitation, and consulting across development and infrastructure teams.

Designs analytical and enterprise data models for Data Quality monitoring on GCP. Defines dbt Core structures, modeling standards, naming conventions, integration principles, and migration mappings. Collaborates with business analysts, data engineers, and test engineers to implement, validate, and document scalable cloud data solutions. Requires extensive data modeling experience, GCP and dbt expertise, SQL knowledge, and strong stakeholder communication.