Cloud Data Architect

Position: Cloud Data Architect

• Location: Kraków, Poland
• Working mode: Initially remote; once the Kraków office is ready, 3-4 days per week onsite
• About our client
• Our client is an Israeli sports-tech company combining advanced data engineering, AI/ML and mathematical modelling to build high-performance real-time sports analytics solutions. The company processes large volumes of real-time data across sports including football, basketball, baseball and American football, turning it into predictions and actionable insights. Its solutions are designed for sports organizations, leagues, broadcasters and other data-driven businesses. The company is now building its new R& D team in Kraków, creating the opportunity to join the organization at an early stage and have a direct impact on its technology and infrastructure.

The RoleAs a Cloud Data Architect, you will take ownership of cloud infrastructure, real-time data processing and the architecture required to support a high-concurrency platform handling millions of users and hundreds of thousands of commands per minute. You will work at the intersection of cloud engineering and data architecture, designing scalable, low-latency and highly available systems. The role combines hands-on technical work with architectural decision-making, performance optimization and collaboration across teams. You will also contribute to the long-term evolution of the platform, including scalability, cost optimization, observability and disaster recovery.

Key Responsibilities

Design, deploy and maintain scalable cloud environments using Infrastructure as Code.

Build and operate real-time, event-driven data pipelines designed for high throughput, fault tolerance and low latency.

Design and optimize data storage and data lake solutions supporting both transactional and analytical workloads.

Develop highly available networking, API gateways, automated failover mechanisms and zero-downtime routing strategies.

Integrate data validation, monitoring and observability into the data pipeline lifecycle.

Identify and resolve performance bottlenecks and data quality or data drift issues.

Drive long-term cloud scalability, cost optimization, disaster recovery and system benchmarking.

Work closely with engineering and data teams to translate business and technical requirements into scalable architecture.

What We’re Looking For5+ years of experience in Cloud Infrastructure, Data Engineering or Distributed Systems Architecture.

Strong experience designing and provisioning infrastructure with Terraform.

Hands-on experience with Docker and Kubernetes.

Experience with cloud platforms, particularly AWS; GCP experience is also relevant.

Strong knowledge of distributed, event-driven data architectures, particularly Apache Kafka and Change Data Capture (CDC).

Experience with object storage such as S3, data lakes and relational and NoSQL/document databases.

Strong programming and scripting skills in Python and Bash; experience with Java or Kotlin is a plus.

Experience designing and optimizing ETL/data pipelines and working with high-volume data processing.

Ability to combine high-level architecture with hands-on performance tuning and implementation.

Strong English communication skills (C1); Polish is not required.

Background in fintech, iGaming or other high-throughput platforms is a nice-to-have.

What We Offer

Opportunity to join a new R& D organization in Kraków and influence its technical foundations from the beginning.

Primarily employment based on an employment contract (UoP), with B2B cooperation also planned as an option later.

Initially fully remote work, followed by a hybrid model with 3-4 days per week from the Kraków office once the office is ready.

Opportunity to work on large-scale, real-time data infrastructure serving millions of users.

International environment and direct collaboration with the company’s leadership and technical teams.

Competitive compensation and equipment necessary to perform the role.

The opportunity to work on complex cloud, data engineering and distributed systems challenges with a strong focus on scalability and performance.