AWS Cloud Platform Engineer

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:

We are looking for AWS Cloud Data Platform Engineers to join a project delivered for our international client operating in a data-driven and cloud-first environment. The client is building modern cloud-native data platforms that enable advanced analytics, business intelligence, and data-driven decision-making across the organization.

This role is a great opportunity to work on scalable AWS-based data platforms, contributing to the design, development, and continuous improvement of modern data ecosystems. You will combine cloud platform engineering, data engineering, and software development to deliver reliable, secure, and high-performance data solutions supporting business-critical processes.

The project combines cloud technologies, data platform development, ETL/ELT engineering, and platform automation within a collaborative international environment. You will work closely with business stakeholders, architects, data engineers, and software development teams to deliver scalable and maintainable cloud-based data solutions.

You will be:

• design, develop, and maintain modern data platforms based on AWS cloud services,
• build and optimize data lakes, lakehouses, and data warehouse solutions,
• develop and maintain ETL and ELT pipelines for batch and streaming data processing,
• integrate data from multiple sources, including databases, APIs, and event-driven systems,
• ensure data quality, reliability, observability, and operational stability across data platforms,
• develop data processing solutions using Python, SQL, Spark, and related technologies,
• work with AWS services such as S3, Glue, Lambda, EMR, Redshift, Kinesis, and similar cloud-native services,
• implement and maintain workflow orchestration using technologies such as Apache Airflow, AWS Step Functions, or equivalent solutions,
• contribute to CI/CD pipelines and deployment automation for data engineering workloads,
• monitor platform health, troubleshoot data-related incidents, and implement preventive improvements,
• optimize platform performance, scalability, and cloud cost efficiency,
• collaborate closely with business stakeholders, architects, analysts, and engineering teams to deliver high-quality data products,
• contribute to engineering best practices, platform standards, automation, and continuous improvement initiatives,
• depending on seniority, take ownership of technical areas, support architectural decisions, and mentor less experienced team members,

Your profile:

• commercial experience in data engineering, cloud engineering, or data platform development,
• strong hands-on experience with AWS cloud services,
• practical experience designing and building cloud-based data platforms,
• experience developing data lakes, lakehouses, or enterprise data warehouse solutions,
• strong experience building ETL and ELT pipelines in production environments,
• practical experience integrating multiple data sources, including relational databases, APIs, and streaming platforms,
• strong programming skills in Python and SQL,
• experience with Apache Spark or similar distributed data processing frameworks,
• practical experience with AWS services such as S3, Glue, Lambda, EMR, Redshift, Kinesis, or equivalent technologies,
• experience implementing workflow orchestration using Apache Airflow, AWS Step Functions, or similar tools,
• understanding of CI/CD practices and deployment automation for cloud-based data solutions,
• experience implementing monitoring, observability, and operational support for data platforms,
• strong understanding of cloud performance optimization and cost management,
• strong communication, collaboration, and problem-solving skills,
• experience working within Agile software delivery environments,

• 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:

• experience working with real-time or event-driven data processing architectures,
• experience with Infrastructure as Code technologies such as Terraform or AWS CloudFormation,
• familiarity with containerization and orchestration technologies such as Docker and Kubernetes,
• experience implementing data governance, security, and compliance practices,
• experience contributing to enterprise-scale cloud data platform architecture,

• 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