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Open nowPosted 69 days ago

Data Engineering Manager

randstadhu423 open roles

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Your applicationOpen nowData Engineering Managerrandstadhu

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The clock on this job

Early applications get read.

11.5% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 69 days ago

The posting

  • A unique professional challenge to taking part in building up a completely new R&D strategic hub from the ground up for a stable, market-leading enterprise.
  • Manager-level benefits including an annual target bonus.
  • Additional perks: a cafeteria allowance, private health insurance package
  • Flexibility: Hybrid working model requiring 3 days of office presence (Budapest) and offering 2 days of home office.
  • 4+ years in a team lead, principal engineer, or management role leading engineers (local and/or remote teams)
  • 5+ years as a hands-on data engineer with recent, demonstrated experience in modern tech stacks
  • Direct experience in change management, scaling teams, remote / hybrid / onsite team coordination
  • Significant production experience with the Databricks Lakehouse on AWS — Unity Catalog, Delta Live Tables, Databricks SQL, and Databricks Workflows. Working knowledge of supporting AWS services (S3, IAM).
  • Deep expertise in data pipeline design, ingestion, transformation, quality assurance, and delivery
  • Professional experience in data-driven organizations with insights and ML/AI as core business functions
  • Hands-on FinOps — owning compute spend, building cost visibility, and driving costoptimization initiatives
  • Working knowledge of data science, ML/AI operations, and model deployment pipelines

Preferred Qualifications:

  • Production AI/ML systems experience where data engineering directly enabled product features
  • Experience managing teams across multiple time zones or geographic locations
  • Familiarity with data governance, compliance, and metadata management at scale
  • Track record of building and scaling data platforms that support analytics and operational intelligence

What We Value

  • Technical depth combined with leadership ability—you build systems and develop people
  • Proven problem-solving and architecture design skills in complex, data-heavy environments
  • Pragmatism over perfection; ability to balance technical excellence with business timelines
  • Collaboration across teams and comfort partnering with analytics, data science, and engineering organizations
  • Stakeholder fluency with SRE, DBA (SI: work with them also), BI/Analytics, Data Science and Product teams
  • AI-leveraged delivery comfortable with the team using Claude Code, Cursor, or Databricks Genie to accelerate pipeline development
  • Lead and mentor a team of data engineers, fostering technical growth and accountability
  • Design and oversee data pipeline architecture from ingestion through delivery, ensuring scalability and reliability
  • Own data quality, cleaning, ETL processes, and delivery mechanisms serving analytics and ML/AI operations
  • Collaborate with data science and MLOps teams to support model development and production deployment
  • Stay current with data engineering best practices and emerging technologies; drive technical decision-making
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