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Sr EM Data Platform; m​/f​/d

in 80331, München, Bayern, Deutschland
Unternehmen: Usercentrics
Teilzeit position
Verfasst am 2026-01-30
Berufliche Spezialisierung:
  • IT/Informationstechnik
    Dateningenieur, Datensicherheit, Daten Analyst, Data Science Manager
Gehalts-/Lohnspanne oder Branchenbenchmark: 60000 - 80000 EUR pro Jahr EUR 60000.00 80000.00 YEAR
Stellenbeschreibung
Stellenbezeichnung: Sr EM Data Platform (m/f/d)

Location

Hybrid in Munich (minimum of 2-3 days a week from the office)

SHAPE THE FUTURE OF PRIVACY WITH USERCENTRICS

Usercentrics is a global leader in data privacy and privacy-led marketing solutions. We believe there is no need for a trade-off between growth and privacy compliance. Our vision is to unlock the potential of data privacy to empower a thriving digital ecosystem. We work with companies to create a healthy balance between data-driven business and privacy-led marketing for every size of enterprise.

Our customers build trust with their users through improved transparency and control to drastically improve marketing and monetization, while achieving full privacy compliance.

We are looking for a Senior Engineering Manager to lead and scale our Data Engineering function. This role has end to end ownership of the data platform and integration architecture. It combines strategic leadership with hands-on expertise in data engineering, ensuring that our data ecosystem is secure, reliable, and optimized for business impact.

As Senior Engineering Manager, you will shape the long term Data Ops strategy. Initially, you will oversee the modernization of our data platform and work closely with Engineering, Product, and Analytics to ensure optimal pipelines and reliable integrations with operational systems and third party tools. Over time, you will design and lead the evolution of the Data Platform to support advanced use cases such as ML Ops, Data Products, and real time analytics.

Your Tasks
  • Initially lead a small team of Data Engineers, providing hands on technical leadership, and progressively scale it into a full data platform team with clear ownership and operating model

  • Own the data warehousing strategy, including the technology stack, core architectural principles, and long term evolution

  • Define and lead the strategy and architecture for integrating the data platform with operational systems and third party tools, ensuring reliable, scalable, and well governed data flows across the organization.

  • Define standards for data ingestion and enrichment, covering reliability, data quality, and fault tolerance across batch and near real time pipelines

  • Lead the design and maintenance of foundational data layers that support multiple downstream use cases, including integrations, operational reporting, and advanced analytics

  • Ensure warehouse schemas, storage patterns, and partitioning strategies support performance, scalability, and cost efficiency across diverse workloads

  • Oversee the data platform governance, including access control, data classification, retention policies, and auditability, aligned with security and privacy requirements

  • Drive observability for pipelines, datasets, and integrations including freshness, volume anomalies, data quality signals, and cost monitoring

  • Enable the team to balance strategic priorities with operational excellence.

You Bring
  • Proven experience defining long term data platform and Data Ops strategies, translating business and technical requirements into scalable architectures

  • Hands on background in data engineering, with the ability to guide technical design decisions, review implementations, and unblock complex engineering problems

  • Deep understanding of data integration patterns with operational systems and third party tools, including APIs, event driven architectures, CDC, and reverse ETL use cases

  • Solid grasp of data quality, reliability, and fault tolerance principles, including idempotency, error handling, and recovery strategies

  • Experience designing and operating enriched data layers that support multiple downstream use cases across analytics, integrations, and advanced data products

  • Strong knowledge of data governance, security, and privacy concepts, including access control, data classification, retention, and auditability

  • Practical experience with observability and monitoring of data platforms, covering pipeline health, freshness, data quality signals, and cost awareness

  • Ability to lead and grow engineering teams, starting with a small group of data engineers and scaling into a broader data platform or Data Ops team

  • Clear communicator with strong stakeholder management…

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