Our client is a US-headquartered, market-leading enterprise in their sector with a multi-decade history and an agile, global network. We are seeking highly skilled Senior Data Engineers to join their expanding Budapest team as key technical individual contributors. In these roles, you will build robust data platform solutions, optimize core pipelines, and directly shape future-proof, data-driven, and AI-enabled infrastructure.
Pozíció leírása / Job description- Pipeline Engineering & Architecture: Design, build, and maintain high-performance, end-to-end data pipelines covering ingestion, processing, quality assurance, and delivery layers. Own critical data architecture decisions and technical implementations.
- Lakehouse & Platform Optimization: Optimize data platform performance, reliability, and cost across Databricks Lakehouse architectures. Apply advanced tuning techniques (partitioning, clustering, materialized views, pre-aggregation) and implement medallion patterns (bronze/silver/gold with published consumer layers).
- ML/AI Operations Enablement: Partner closely with data science, analytics, and ML/AI operations teams to build platforms and pipelines that enable model development, training, feature engineering, and production deployment.
- Data Governance & Observability: Establish and drive best practices around data quality assurance, governance, metadata management, validation frameworks, and platform observability.
- Infrastructure & CI/CD: Manage infrastructure as code and CI/CD practices across Git-based deployment workflows.
- Technical Leadership & Innovation: Influence technical direction, mentor junior engineers, evaluate and adopt emerging technologies, and effectively partner across both technical and non-technical stakeholders.
Required Qualifications
- Experience: 10+ years of professional data engineering experience building production-grade data systems.
- Core Stack: Deep expertise with Databricks and Apache Spark, with proven experience optimizing complex distributed workloads.
- AWS & Lakehouse: Production experience with the Databricks Lakehouse on AWS (Unity Catalog, Delta Lake, Delta Live Tables, Databricks SQL) alongside core supporting AWS services (S3, IAM, Lambda, RDS).
- Languages & Querying: Expert SQL skills and strong programming proficiency in Python (PySpark). (Scala knowledge is a plus).
- Architectural Mastery: Solid understanding of Lakehouse and data warehouse architectures, medallion patterns, and dimensional modeling.
- Data Domain Background: Professional experience at companies where data ingestion, delivery, and consumption are core business functions.
- Collaboration & Leadership: Strong communication skills with a proven ability to mentor junior engineers, influence technical direction, and partner across diverse stakeholders.
Preferred Qualifications
- Hands-on experience with data science workflows (feature engineering, model input preparation, evaluation support) and production ML/AI operations (ML monitoring, retraining pipelines, model deployment).
- Exposure to LLM/generative AI infrastructure, RAG systems, or embeddings pipelines in production.
- Infrastructure-as-Code and CI/CD proficiency (Databricks Asset Bundles, Terraform, Git-based workflows).
- Advanced knowledge of open table formats (Delta Lake, Apache Iceberg), data quality frameworks, metadata management, and observability/monitoring tools.
- Cloud cost optimization expertise and/or contributions to open-source data engineering projects.
Technical Skills & Domain Knowledge
- Distributed computing fundamentals, large-scale data processing optimization, and performance benchmarking/profiling.
- Real-time streaming, event-driven architectures, API design, and integration patterns for data consumption.
- Security, encryption, and compliance requirements for sensitive enterprise data.
- Familiarity with AI-assisted development tools (Claude Code, Cursor, Databricks Genie).
- High Technical Impact: A unique professional challenge to shape the core data platform for a stable, market-leading global enterprise.
- Competitive Compensation: Senior level base salary and 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.
Ildikó Mező-Mészáros - ildiko.mezo-meszaros@randstad.hu
Lili Wenner - lili.wenner@randstad.hu
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