Xebia is a global AI-first, digital transformation, and engineering partner. With over 25 years of experience and a team of 5,000 professionals across 16 countries, we help organizations design and build scalable products, platforms, and data-driven solutions.
We specialize in Artificial Intelligence, Data and Cloud, Intelligent Automation, and Digital Products, combining deep technical expertise with a strong focus on engineering excellence and a people-first culture.
In the CEE region, we’re a team of nearly 1,000 experts delivering modern applications, data platforms, and AI solutions for clients such as McLaren, Aviva, Deloitte, Spotify, Disney, ING, UPS, Tesco, Truecaller, AllSaints, Volotea, Schmitz Cargobull, Allegro, InPost, and many, many more. We work with leading technologies including AWS, Azure, GCP, Databricks, and Snowflake, and combine strong engineering culture with a consulting mindset and a continuous focus on growth and knowledge sharing.
You will be:
- designing and developing an in-house AI-powered Analytics Agent running in Devin.
- implementing agent capabilities supporting common analytics engineering tasks, such as modifying data marts, ingesting additional data sources, and running dbt workflows.
- integrating the agent with GitHub to support code changes and development activities.
- adapting and configuring an existing dbt project to run reliably in Databricks.
- analyzing and resolving compatibility differences between the existing Snowflake implementation and the new Databricks environment.
- developing and testing new agent capabilities against the evolving dbt and Databricks setup.
- defining safe and reliable workflows for AI-assisted changes to data models, ingestion pipelines, and analytics code.
- applying engineering best practices around code quality, testing, code reviews, observability, security, and maintainability.
- implementing appropriate validation mechanisms and guardrails for actions performed by the AI agent.
- troubleshooting issues across Devin, GitHub, dbt, Databricks, Snowflake, MySQL, and related integrations.
- collaborating with the client’s technical stakeholders and engineering teams to ensure the solution meets business and operational needs.
- documenting the architecture, implementation decisions, operating procedures, and common support scenarios.
- preparing knowledge-transfer materials to enable other engineers to handle future support requests.
Your profile:
- senior-level experience in data engineering, analytics engineering, or a related field.
- strong hands-on experience with dbt, including data modelling, testing, documentation, dependency management, and troubleshooting.
- commercial experience with Databricks.
- good understanding of Snowflake and experience adapting or migrating analytics workloads between data platforms.
- strong SQL skills and experience designing data marts and analytical data models.
- experience building and maintaining data ingestion pipelines and working with relational data sources such as MySQL.
- strong knowledge of Git and GitHub-based development workflows.
- experience integrating tools through APIs, command-line interfaces, or automation frameworks.
- experience developing AI-powered engineering tools, AI agents, or agent-based automation.
- understanding of software engineering best practices, including automated testing, code reviews, CI/CD, logging, monitoring, and error handling.
- ability to design reliable guardrails and validation mechanisms for AI-generated or AI-executed code changes.
- ability to work independently and take end-to-end ownership of technically complex solutions.
- strong problem-solving and troubleshooting skills.
- strong written and verbal communication skills in English.
- documentation-first mindset and ability to create practical technical and support documentation.
- ability to collaborate effectively with client stakeholders and engineering teams.
Work from the European Union region and a work permit are required.
Nice to have:
- hands-on experience with Devin or other autonomous AI software engineering platforms,
- experience building tools that enable AI agents to interact with repositories, data platforms, or development environments,
- knowledge of agentic workflows, tool calling, prompt design, context management, and AI-agent evaluation,
- experience implementing human-in-the-loop approval workflows for AI-driven changes,
- familiarity with data contracts, data lineage, metadata management, and data quality frameworks,
- experience with CI/CD pipelines for dbt and Databricks,
- knowledge of Databricks Asset Bundles, Databricks Workflows, or Unity Catalog,
- experience designing operational and support models for internally developed engineering tools,
- experience mentoring engineers or transferring technical ownership of a solution to an internal team.
Recruitment Process:
CV review – HR call – Interview – Client Interview – Decision