Data Engineering Manager
PwC is hiring a Data Engineering Manager in Warsaw, Poland. Level rates it ; you can apply on PwC.
Level's read on this job
- Level rates this data engineer role Works on AI ●●●○: the daily work is on AI products, without building the model.
- The posting allows some remote work.
- Tools and skills the posting cites include Weaviate, pgvector and Databricks.
- Level classifies it as a lead role.
- Level lists 3,700+ other open roles at PwC.
AI in this role
Data Engineering Manager leading cloud data platforms and co-creating data foundations for GenAI, agentic systems, and ML use cases.
Job Description & Summary
In PwC Poland’s Digital&IPA we create innovative applications and deliver data-driven insights by combining software engineering expertise with advanced data analysis, including GenAI applications. Our capabilities span across data science, UX design, business intelligence, data engineering, frontend & backend development and cloud solutions. Our multidisciplinary approach ensures that we deliver comprehensive, impactful solutions that drive business success. We carry out projects in various industries, including banking, insurance and retail, supporting clients from Europe, the USA, South America, and Asia.
We are looking for:
Data Engineering Manager
Your future role:
- A hybrid leadership position split equally between business development and hands-on technical delivery on client projects,
- Building strong cooperation with adjacent teams (AI Engineering, Software Development, Cloud) to design integrated, cross-domain offerings,
- Co-creating and evolving our "Data for AI" proposition: how we deliver data foundations for GenAI, agentic systems, and ML use cases,
- Supporting pre-sales activities: client meetings, solution scoping, estimations, proposals, and RFP responses,
- Contributing to thought leadership: internal knowledge sharing, conference talks, articles, and client workshops,
- Supporting recruitment and growth of the Data Engineering practice,
- Acting as a Data Architect, Tech Lead, or SME on client engagements – designing end-to-end cloud data platforms on Azure, including data architectures powering GenAI and agentic solutions (vector databases, RAG pipelines, AI-ready data products),
- Guiding team members, reviewing architectural decisions and code, and maintaining regular communication with senior client stakeholders.
Apply if you have:
- At least 8 years of professional experience in data engineering, including senior, lead, or architect roles,
- Proven experience in client-facing or pre-sales activities (scoping, estimations, proposals, solution presentations),
- Hands-on experience designing and delivering cloud data platforms, like Databricks, MS Fabric, Snowflake,
- Working experience processing large volumes of structured, semi-structured and unstructured data with Python, Spark and SQL
- Solid understanding of data warehousing concepts and experience in data modeling,
- Familiarity with Data Governance topics: data quality assurance, data lineage, versioning, cataloging, privacy etc.,
- Practical understanding of how data engineering supports GenAI and agentic use cases (e.g. vector stores, RAG pipelines, feature/context delivery for agents),
- Excellent communication and stakeholder management skills, including with senior business audiences,
- Min C1 in English, written and spoken,
- Fluency in Polish for internal communication,
- Familiarity with Scrum or other agile PM methodologies,
Nice to have:
- Hands-on experience with GenAI applications as a developer (e.g. RAG, agentic workflows, MCP or similar tool/data integration patterns),
- Experience with vector databases (e.g. Azure AI Search, Weaviate, pgvector) and document databases (MongoDB, DynamoDB),
- Experience cooperating with AI/ML or software engineering teams on integrated solutions,
- Relevant certifications (e.g. Databricks Certified Data Engineer Professional, Azure Solutions Architect).
By joining us you gain:
- Work flexibility – hybrid working model (min. 2 days in the office per week), flexible start of the day, workation, sabbatical leave,
- Development and upskilling - our full support during onboarding process, mentoring from experienced colleagues, training sessions, workshops, certification co/financed by PwC e.g. Data Engineering learning paths with MS Azure or Databricks and conversations with native speaker,
- Wide medical and well-being program - a medical care package (incl. physiotherapy, discounts on dental care), coaching, mindfulness sessions, psychological support, education through dedicated webinars and workshops, financial and legal advice,
- Possibility to create your individual benefits package (a.o. lunch pass, insurance packages, concierge, veterinary package for a pet, massages) and access to a cafeteria - vouchers, discounts on IT equipment and car purchase,
- 3 paid hours for volunteering per month,
- Additional paid Birthday Day off,
- And when you start enjoying PwC as much as we do, you may recommend your friend to work with us.
Recruitment process:
- Apply,
- Have a conversation with our Recruiter during a short HR screening call,
- Get to know each other better during an interview with our Hiring Manager.
With any queries please contact us pl_ITrecruitment@pwc.com.
Your personal data will be processed for recruitment purposes by PwC Advisory spółka z ograniczoną odpowiedzialnością sp.k. or another PwC entity which runs a recruitment process - (list of entities). If you have given separate consent, data will also be processed for other purposes in accordance with the content of the consents granted. Full information about processing your personal data is available in the Privacy Policy.
#LI-PZ1 #LI-Hybrid
How we rate this
Data Engineering Manager at PwC rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in data engineer jobs at this level
Pay
- Typical data engineer job
- $147k
This posting doesn't list pay. Use the typical data engineer figure when pay comes up. See data engineer jobs
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Tell me about a project where data engineering was part of your work. What did you do?
- Tell me about a project where data architecture was part of your work. What did you do?
- Tell me about a project where genai was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: RAG, AI agents, Data Engineering, Data Architecture, and GenAI. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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