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Open nowPosted 41 days ago

Senior Data Engineer

randstadhu423 open roles

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Your applicationOpen nowSenior Data Engineerrandstadhu

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This job: posted 41 days ago

The posting

  • 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 2 days of office presence (Budapest) and offering 3 days of Home Office per week.

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).
  • 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.
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