AWS Data Engineer with Databricks

Location: Bulgaria

Department: Delivery

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. 

About project:

Our client is a large, technology‑driven logistics and e‑commerce services organization operating at significant scale. The company processes high volumes of operational and analytical data daily and invests heavily in modern cloud‑based data platforms. Data is a critical enabler for business performance, decision‑making, and continuous optimization across the organization.

You will join a Data Services / Data Engineering team responsible for designing, building, and operating scalable data solutions on a modern cloud analytics platform. The team supports multiple business domains by delivering reliable, well‑governed, and high‑performance data pipelines.

You will be:

• designing, developing, and maintaining scalable data pipelines using Python and PySpark,

• building and operating Delta Live Tables pipelines in both streaming and triggered modes,

• selecting and configuring appropriate Databricks compute for different workload types,

• implementing and maintaining data governance using Unity Catalog across Databricks workspaces,

• diagnosing, analysing, and optimising slow SQL queries on large Delta tables,

• defining, packaging, and deploying Databricks resources using Databricks Asset Bundles,

• collaborating closely with other data engineers, analytics teams, and platform stakeholders,

• contributing to best practices around performance, reliability, and maintainability of data solutions

 

Your profile:

• strong experience with AWS Databricks,

• proven experience building scalable data pipelines using Python and PySpark,

• hands‑on experience designing and operating Delta Live Tables pipelines,

• solid understanding of Databricks compute options and the ability to choose the right configuration for specific workloads,

• ability to diagnose and optimise slow SQL queries on large Delta tables using a structured approach,

•  

  experience using Databricks Asset Bundles for infrastructure and deployment as code,

• experience working with Unity Catalog to centralize data governance,

• experience working in complex, data‑intensive environments with large datasets,

• good understanding of data modeling and data quality concepts,

• practical experience using AI-powered assistants (e.g. Claude Code, GitHub Copilot, Cursor) to improve productivity, quality, or decision-making in software delivery,

• work from the European Union region and a work permit are required.

Nice to have:

• familiarity with CI/CD practices for data platforms,
• experience collaborating in cross‑functional, agile teams,
• experience with streaming data architectures,
• exposure to data platform cost optimization,
• experience mentoring or supporting other data engineers,
• knowledge of wider AWS data ecosystem services.

• experience applying GenAI in a more structured way within the SDLC, including defined workflows, prompt patterns, or tool integrations embedded into daily work,

• interest in and familiarity with emerging AI-driven practices (e.g. agent-based workflows, automation patterns, AI-augmented development), with a willingness to explore and experiment beyond standard approaches.

Recruitment Process:

CV review – HR call – Interview – Client Interview – Decision