Senior Manager, Data Engineering (m/f/d)
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 Manager, Data Engineering to lead and scale our Data Engineering team. This role owns the data platform and integration architecture end to end, combining strategic leadership with hands-on, player-coach expertise in data engineering, ensuring our data ecosystem is trusted, reliable, well-governed, and optimized for business impact as our SaaS business scales.
You will shape our long-term data platform and DataOps strategy. Initially, you will lead the modernization of our data platform, working closely with Engineering, Product, Analytics, Commercial, and Finance to deliver optimal pipelines and reliable, well-governed integrations with the operational systems and third-party tools that run a modern SaaS company. Over time, you will design and lead the evolution of the platform toward advanced use cases such as ML Ops, Data Products, and real-time analytics, while growing a small team of Data Engineers into a fully-fledged data platform team.
The challenge
Our data is complex. It is high-volume, multi-source, time-sensitive, and often messy. The challenge is to build a data engine that is:
- Trusted: accurate, governed, explainable
- Fast: optimized performance and cost, low latency where it matters
- Composable: clean models that scale with new products
- Self-serve: enables Product, Analytics, and GTM teams
- Reliable: observable, resilient, and operationally excellent
Your Tasks
- Lead a team of Data Engineers as a hands-on player-coach, growing it into a fully-fledged data platform team with clear ownership and ways of working, and helping it balance strategic priorities with operational excellence
- Own the data warehousing strategy end to end, the stack, the architectural principles, and where the platform goes next as our SaaS business scales, ensuring schemas, storage patterns, and partitioning choices deliver performance and cost efficiency across very different workloads
- Set the strategy and architecture for connecting our data platform to the operational systems and third-party tools that run a modern SaaS company, delivering high-quality systems with reliable, well-governed data flows
- Raise the bar on data ingestion and enrichment, defining what "good" looks like for reliability, data quality, and fault tolerance across batch and near-real-time pipelines, and design and evolve the foundational data layers that power everything downstream: integrations, operational reporting, and advanced analytics
- Own platform governance (access control, data classification, retention, auditability) aligned with security and privacy requirements, and build real observability into pipelines, datasets, and integrations: freshness, volume anomalies, quality signals, and cost
- Partner cross-functionally, communicating clearly with stakeholders on tradeoffs, risks, and timelines, and influence the broader organization on data quality, trust, and accountability
Requirements
- 10+ years of experience directly leading a data engineering or data platform team specifically, not just general engineering management, in a fast-paced, high-growth environment, with proven ability to scale teams and systems through hiring, process, architecture, and delivery
- Experience working in SaaS companies
- Player-coach mindset with prior experience as an individual contributor: hands-on expertise in data engineering, able to guide technical design decisions, review implementations, and unblock complex engineering problems
- Proven experience defining long-term data platform and DataOps strategies, translating business and technical requirements into scalable architectures, and building modern data platforms (ELT, enriched modeling layers, self-serve analytics) that support multiple downstream use cases across analytics, integrations, and advanced data products
- Deep understanding of data integration patterns with operational systems and third-party tools, APIs, event-driven architectures, and reverse ETL, with hands-on familiarity with BigQuery and dbt
- Solid grasp of data quality, reliability, and fault tolerance principles (idempotency, error handling, recovery strategies), and practical experience with observability and monitoring of data platforms: pipeline health, freshness, data quality signals, and cost awareness
- Strong knowledge of data governance, security, and privacy concepts, including access control, data classification, retention, and auditability
- Clear communicator with strong stakeholder management skills, able to align Engineering, Product, Analytics, Commercial, and Finance around shared ownership and priorities, across technical and non-technical audiences, comfortable operating in ambiguous environments, balancing short-term delivery with long-term platform evolution, defining operating models, ownership boundaries, and on-call responsibilities, and passionate about fostering a culture of innovation, learning, and continuous improvement
- Exposure to AI/ML or data science workflows; hands-on AI/ML experience is a plus
Don’t meet every single requirement? Studies have shown that women and people of colour are less likely to apply to jobs unless they meet every single qualification. At Usercentrics we are dedicated to building a diverse, inclusive and authentic workplace, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles.
Benefits
Why join Usercentrics?
- Joining Usercentrics means becoming part of a fast-growing, diverse and international team of tech enthusiasts and entrepreneurially-minded who build our success story together
- Company culture is important to us - we strive to continuously develop a positive, vibrant and inspiring environment that enables everyone to thrive both personally and professionally
- Get involved! We have plenty of initiatives and love to see people from all department enthusiastically participating and shaping our future together in different cross-department projects
- Your work-life balance is important to us too - we offer flexible working hours, hybrid working and the possibility of workcations (in accordance with our company policy)
- We always remember to have fun along the way, both in our day-to-day work and at our regular team events on site in our offices in Munich, Copenhagen, Odense, Lisbon, Prague, Buenos Aires and New York.
Skills
As published by workable · 6 questions · 6 written answers
First name, Last name, Email, Resume, Summary, How many years of experience do you have in the SaaS industry?, Are you legally authorized to work in the job location without visa sponsorship?, Are you currently in the job location?, Our team works in a hybrid model, with 2–3 days per week in the office. Does this work for you?, Cover letter
- Can you share an example of how you've developed and implemented a long-term data platform or DataOps strategy? What were the key business and technical goals, and how did you ensure your architecture could scale with the organization's needs?
- Could you describe your hands-on experience in data engineering, particularly how you've contributed to technical design decisions and helped resolve complex engineering challenges?
- What experience do you have designing and maintaining data layers that support analytics, integrations, or advanced data products? Can you provide an example of how your work enabled multiple downstream use cases?
- Can you tell us about your experience building and scaling engineering teams? How did you approach growing a small group into a larger, high-performing data platform or DataOps team?
- Have you been involved in establishing operating models or defining ownership and on-call responsibilities for data platform teams? If so, what approach did you take and what were the outcomes?
- How have you managed the balance between delivering short-term results and maintaining a vision for long-term platform evolution and architectural integrity?
