Cloud Data Modelling Engineer

PwC Polska; Warszawa; Hybrid; PLN 180,000 - 250,000; Full time

Benefits offered by this job

Hybrid work

Professional development

Medical & wellbeing program

Volunteering hours

Birthday day off

Job summary

PwC Polska is seeking a Cloud Data Modelling Engineer to design logical and physical data models and to enable scalable enterprise data platforms. You will collaborate with data engineers to move data from legacy systems to reference platforms and support data architects on modern architectures.

Ideal candidates have at least 5 years in cloud data modelling, hands-on Databricks/Snowflake experience, Python (PySpark) and SQL skills, and business English proficiency.

Qualifications

• 5+ years in cloud data modelling and data engineering roles or related fields.
• Experience with Databricks, Snowflake or MS Fabric and data pipelines.
• Ability to translate functional requirements into logical and physical models.
• Proficiency in Python (PySpark) and SQL for data transformation.

Responsibilities

• Design conceptual and logical data models and develop physical models.
• Build relational, NoSQL and wide variety of data stores for enterprise platforms.
• Create data entity diagrams and data workflows across domains.
• Support modern data architecture with harmonized and enriched data.
• Collaborate with data engineers to migrate data from legacy systems to reference platforms.
• Conduct data quality assessments across multiple systems.

Skills

Cloud data modelling

Data engineering

Python (PySpark)

SQL

Business English

Tools

Databricks

Snowflake

MS Fabric

Azure Data Factory

Azure Synapse Analytics

Informatica

Stibo

Syndigo

Profisee

CluedIn

Job description

We are looking for:

Cloud Data Modelling Engineer

Your future role:

• Designing conceptual and logical data models and developing these into physical data models to enable the development and scaling of enterprise data platforms,
• Building relational, distributed, hierarchical, object‑orientated and NoSQL databases and configuring level of granularity, degree of normalization and relationships between data entities therein,
• Creating data entity diagrams and data workflows across multiple data domains, systems of record and data assets, to meet the functional requirements of end business users from key business functions like Finance, Supply Chain, Marketing etc.,
• Supporting Data Architects to design modern data architecture for enterprise data platforms that rely on harmonized, standardized and enriched data,
• Working alongside a team of data engineers to extract, load and transfer data from legacy systems of record to systems of reference i.e., reports, applications, dashboards,
• Conducting comprehensive data assessments across multiple systems to identify and remediate data quality issues relating to master records and transactional data,
• Developing team expertise in the automation of building physical data models using tools such as Azure Databricks Agentic Bricks, and/or MS Foundry, Fabric and Ontology,
• Sharing perspectives with the wider team on data modelling techniques and frameworks, e.g., Kimball Dimensional modelling,
• Developing data standards and policies specifically around Master Data Management and Data Quality.

Apply if you have:

• Minimum of 5 years in cloud data modelling and data engineering roles, although this can be considered alongside other relevant experience e.g., Business Intelligence, Data Science etc.,
• Hands‑on experience of using data transformation tools e.g., Databricks, Snowflake, MS Fabric etc.,
• Experience in conducting data modelling activities with a focus on developing Logical Data Models into Physical Data Models using frameworks like Kimball Dimensional Modelling,
• Familiarity with writing data transformation scripts using Python i.e. PySpark and SQL to query databases,
• Proficiency in Business English at B2 level or above, with confidence to write technical documentation e.g., designing documents with support from Project Manager,
• Proven experience in conducting data assessments across multiple data domains, including Product, and Customer and systems of record e.g. ERPs and CRMs,
• Experience in building data warehouses, data lakes and data lakehouses using tools like Azure Data Factory and Azure Synapse Analytics,
• Some familiarity with using Master Data Management tools like Informatica, Stibo, Syndigo, Profisee, CluedIn etc.,
• Experience with new technologies and AI‑based tools demonstrated in your daily work (e.g., task automation, information analysis, content creation).

Nice to have:

• Cloud certifications in Azure, AWS, or Google Cloud,
• Data Governance certifications e.g., DAMA,
• Evidence of training using online platforms e.g. Coursera, Udemy, Datacamp etc.

By joining us you gain:

• Work flexibility - hybrid working model, 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 and conversations with native speaker,
• Wide medical and wellbeing program - medical care package (incl. freedom of treatment, physiotherapy, discounts on dental care), coaching, mindfulness, psychological support, education through dedicated webinars and workshops, financial and legal counseling,
• Possibility to create your individual benefits package (a.o. lunch pass, 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,
• Talk to our recruiter on a short HR screening call,
• Get to know us better during the interviews (technical interview and cultural fit interview).

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.