Principal Data Engineer

• High Technical Impact: A unique professional challenge to shape the core data platform for a stable, market-leading global enterprise.

• Competitive Compensation: Senior/Principal level base salaryand annual target bonus.

• Benefits Package: Cafeteria allowance and private health insurance package.

• Flexibility: Hybrid working model requiring 3 days of office presence (Budapest) and offering 2 days of Home Office per week.

Responsibilities

• Architecture & Implementation: Architect and implement production-grade data solutions on AWS and Databricks- from lakehouse design through pipeline delivery, ensuring high performance, reliability, and cost efficiency.

• Technical Strategy & Standards: Translate the overall data strategy into concrete technical blueprints, reference architectures, and actionable implementation plans. Establish and enforce architectural standards, data modeling conventions, and engineering best practices.

• Data Platform Foundations: Own the design and buildout of core data platform components- including ingestion frameworks, transformation layers, data quality enforcement, and serving patterns.

• ML/AI Enablement: Design data pipelines and lakehouse structures that directly enable Machine Learning and AI workloads, collaborating closely with Data Science and MLOps teams.

• Platform FinOps: Drive FinOps at the architecture level- right-sizing compute/storage, building cost attribution models, and implementing cost optimization patterns across the platform.

• Technical Mentorship: Mentor data engineers through architectural guidance, pair programming, and code reviews without carrying people-management responsibilities.

Requirements

• Core Experience: 8+ years as a hands-on data engineer or data architect with recent, demonstrated production delivery in modern cloud-native stacks.

• Senior / Principal Track: 3+ years operating at a Staff, Principal, or Senior Architect level as an individual contributor driving technical direction.

• Databricks & AWS Expertise: Deep production experience with the Databricks Lakehouse on AWS- Unity Catalog, Delta Live Tables, Databricks SQL, Databricks Workflows, and Structured Streaming. Strong working knowledge of supporting AWS services (S3, IAM, Glue, Lambda, Kinesis, Redshift).

• Data Modeling & Observability: Expert-level data modeling skills (Dimensional, Data Vault, Lakehouse paradigms)and proven experience building data quality frameworks and automated testing into pipeline architectures.

• Infrastructure-as-Code: Proficiency with Terraform, CloudFormation, or Databricks Asset Bundles.

• FinOps Approach: Hands-on experience designing architectures that control compute spend and optimize resource utilization.

Preferred Qualifications:

• Production experience building data infrastructure that directly enabled ML/AI product features (e.g., feature engineering pipelines, training datasets, model monitoring).

• Experience with real-time or near-real-time data architectures (streaming ingestion, CDC, event-driven patterns).

• Familiarity with Data Mesh principles, domain-oriented platform design, and enterprise data governance (access controls, PII handling, lineage).