Senior Data Engineer (AI Usage & Telemetry)

Location: Poland, Georgia, Serbia, and other eligible locations (primarily Europe)

Engagement: Full-time

Start Date: ASAP

English: B2+ (confident spoken communication required)

Experience: 5+ years in Data Engineering

About the Role

We are looking for a Senior Data Engineer to join an international project focused on building a centralized data platform for tracking and analyzing AI usage across an organization.

Our client has been actively adopting AI tools across different departments and business functions, including engineering, sales, and other internal teams. As AI adoption has expanded, the company has faced a new challenge: there is currently no unified way to understand which AI tools are being used, by whom, how frequently, and at what cost.

The goal is to bring AI usage data from multiple platforms, services, and internal systems into a centralized repository. This will provide a reliable foundation for tracking AI adoption, monitoring resource consumption, and delivering meaningful metrics and reporting to company leadership.

This is an internal data initiative focused on AI usage visibility and analytics, rather than the development of AI models or customer-facing AI products.

In this role, you will take ownership of the Data Engineering foundation behind these capabilities: investigating available data sources, developing integrations, building reliable data pipelines, designing storage and data models, and ensuring data quality and consistency.

The project is at an early stage, and the technology stack is open. We are looking for an experienced Data Engineer who can independently explore technical approaches, make architectural decisions, and build practical solutions from high-level business requirements.

Practical experience working with AI systems and AI integrations is important for this role. You should understand how AI services connect with other systems, how data can be accessed through APIs or connectors, and how information from different AI platforms can be collected and consolidated.

Previous experience building dedicated AI usage monitoring or analytics solutions would be a strong advantage, but is not required.

What You'll Do

• Investigate AI data sources: Explore different AI platforms, internal systems, APIs, connectors, logs, and other available mechanisms for collecting AI usage information.
• Build data integrations: Develop and maintain integrations with AI services, internal corporate systems, and other relevant data sources.
• Develop data pipelines: Design, implement, and optimize reliable ETL/ELT pipelines for collecting, transforming, and consolidating data from multiple independent sources.
• Centralize AI usage data: Collect, clean, normalize, and structure information from different AI tools and systems into a unified data repository.
• Design data architecture: Define appropriate approaches to data processing, storage, and integration, and select technologies based on project requirements.
• Develop data models: Design and maintain data models that support consistent analytics, reporting, and business metrics.
• Enable AI usage analytics: Prepare data for tracking AI adoption, user and team activity, request volumes, token consumption, model usage, and associated costs.
• Ensure data quality: Implement validation, monitoring, and reliability mechanisms to maintain accurate, consistent, and trustworthy data.
• Optimize data processing: Investigate technical issues, improve pipeline performance, and ensure reliable and scalable data integrations.
• Support reporting: Provide structured datasets and metrics that can be used for dashboards, management reporting, and business decision-making.
• Take ownership of solutions: Independently clarify requirements, investigate technical challenges, propose solutions, and drive their implementation.
• Collaborate with stakeholders: Work with technical specialists and business teams to understand data needs and translate them into practical engineering solutions.

What We're Looking For

• 5+ years of professional experience in Data Engineering.
• Strong hands-on experience designing, developing, and maintaining ETL/ELT pipelines and data processing workflows.
• Solid SQL skills and practical experience with databases, data warehouses, and analytical data storage.
• Experience integrating data from multiple independent sources, including APIs, external platforms, and internal systems.
• Hands-on experience working with AI/LLM systems or integrating AI services into applications, data platforms, or internal workflows.
• Practical understanding of AI integrations, including APIs, connectors, and mechanisms for exchanging or retrieving data from AI systems.
• Strong programming skills in at least one language suitable for data integration and processing. No specific programming language is required.
• Experience working with APIs and structured data formats.
• Solid understanding of data modeling, data transformation, and data quality principles.
• Understanding of data pipeline architecture, orchestration, monitoring, reliability, and scalability.
• Ability to independently analyze requirements, design technical solutions, evaluate trade-offs, and select appropriate technologies.
• Experience preparing and structuring data for analytical metrics, dashboards, and reporting.
• Strong analytical and problem-solving skills, including the ability to work with evolving or ambiguous requirements.
• Proactive mindset, ownership, and the ability to drive tasks from high-level requirements to working solutions.
• Strong communication skills and the ability to collaborate with technical and non-technical stakeholders.
• English at B2+ level, with confident spoken communication and the ability to discuss technical decisions with stakeholders.

Nice to Have

• Experience collecting and processing AI usage data, including request volumes, token consumption, model usage, and associated costs.
• Experience building centralized AI usage monitoring, analytics, or reporting solutions.
• Familiarity with MCP servers and other AI-specific integration mechanisms.
• Experience with telemetry, observability, monitoring, or usage analytics systems.
• Experience integrating multiple AI platforms or services into a unified data environment.
• Experience designing and building data platforms or analytical solutions from scratch.
• Experience supporting dashboards and reporting based on data consolidated from multiple sources.

What We Offer

• Full-time engagement: Join an international project focused on a strategically important area of AI adoption.
• Meaningful technical impact: Help establish the data foundation that enables the client to understand and optimize AI usage across the organization.
• Technology flexibility: Work with an open technology stack and contribute to architectural and engineering decisions.
• Ownership and autonomy: Take responsibility for Data Engineering challenges and influence how solutions are designed and implemented.
• International collaboration: Work with engineers and business stakeholders in an international environment.
• Remote work: Work remotely from Poland, Georgia, Serbia, or other eligible locations, primarily across Europe, with the option to work from our coworking space in Warsaw.
• Working hours: Work with an orientation toward the Polish time zone, with the exact schedule coordinated with the team.