PwC Polska
Data Engineering Manager
- Compensation
- Not disclosed
- Location
- Warszawa, Poland
- Work setup
- Hybrid
- Seniority
- Lead
- Employment
- Not specified
- Experience
- 8+ years
- Category
- Data engineering
- Posted
Skills
Must 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).
Full description
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.
Data Engineering Manager
Responsibilities
- 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.
Qualifications
- 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),