Senior Data Engineer (AWS)

Location: Bulgaria, Czechia, Hungary, Moldova, Romania, or Slovakia. Work from the European Union region and a work permit are required.

Company

hirify.global is a global AI-first digital transformation and engineering partner delivering data platforms, AI solutions, cloud services, and digital products.

What you will do

• Design and build consumable data assets and data products on an AWS data platform, including schemas, documentation, ownership, and handover to product teams.
• Work with product engineering teams to define data contracts, map domain ownership, and integrate data into the wider platform landscape.
• Onboard bronze- and silver-layer data from legacy warehouses using internal agentic tooling.
• Build streaming and batch pipelines with Flink, SQL, Python, Kafka, Iceberg, and DynamoDB on AWS.
• Develop tooling that accelerates adoption of the data platform and help teams debug pipeline and batch-job issues.
• Drive technical initiatives, promote engineering best practices, and enable teams through pairing, reviews, and hands-on guidance.

Requirements

• Solid data engineering experience with large-scale data platforms and big data workloads.
• Hands-on experience with stream processing, event-driven architecture, schema management, and batch engineering.
• Practical experience with AWS, Kafka, Iceberg, DynamoDB, and core AWS services.
• Exposure to Apache Flink using SQL or Python, with the ability to become productive quickly.
• Lead Engineer-level experience, including setting technical direction, owning outcomes, and delivering initiatives end to end.
• Fluent English and the required European Union work authorization.

Nice to have

• Experience with AI-assisted engineering, agentic tooling, data mesh concepts, data contracts, and federated governance.
• Awareness of Kubernetes and experience in data platform enablement or internal developer experience.
• Experience with high-volume operational or transactional environments where data volume and latency are important.
• Structured GenAI workflows, prompt patterns, tool integrations, and interest in agent-based automation.

Culture & Benefits

• Work in a lean platform team of approximately six engineers with a part-time Product Manager.
• Collaborate closely with product engineering teams and platform users.
• Share knowledge and raise engineering standards through practical guidance, pairing, and reviews.
• Work with modern AWS, Databricks, Snowflake, and AI technologies in a consulting and engineering environment.

Hiring process

• CV review.
• HR call, interview, and client interview.
• Final decision.