Data Engineer

Job Description & Summary

PwCis a global network of more than370000 professionals in 149 countriesthat turns challenges into opportunities. We create innovative solutions in audit consulting tax and technology combining knowledge from all over the world.

Join PwCs pioneering data & AI team and help build the data foundations behind impactful AI solutions. Were growing quickly due to a strong pipeline of client work and were hiring acrossmultiple seniority levels from junior to senior Data Engineers and Data Scientists.

Across our projectsPython is the core skillwe expect. Depending on your strengths you may focus more ondata engineering(pipelines platforms SQL) ordata architecture(designing scalable data solutions). Many roles also include full-solver flexibility contributing where needed including automation integration work or enabling AI/GenAI use cases on modern platforms (including Microsoft technologies where relevant).

Key responsibilities:

• Data Pipeline Design & Development:Design implement and maintain scalable data pipelines and ETL/ELT processes primarily using Python and Spark (PySpark) to ingest transform and deliver data from various sources into analytics and ML platforms.
• Data Modelling & Warehousing:Design and optimize data models (e.g. star/snowflake schemas) build and manage data warehouses and data lakes and ensure data structures support reporting analytics and ML use cases.
• Data Preparation for ML:Collaborate closely with data scientists and ML engineers to understand data requirements implement robust preprocessing and feature engineering steps and ensure datasets are clean consistent and suitable for machine learning models.
• Performance & Reliability:Optimize data processing jobs and SQL queries for performance and cost efficiency monitor data pipelines in production and ensure reliability scalability and adherence to SLAs.
• Client Collaboration:Work directly with clients across Western Europe and the USA to understand their data landscape and requirements translate business needs into technical designs and provide expert guidance on data architecture and best practices.
• Governance Quality & Security:Implement data quality checks validation frameworks and governance standards; ensure data security privacy and compliance in line with PwC and client requirements.
• Learning & Development:Stay at the forefront of data engineering big data and cloud technologies continuously improving existing solutions tools and processes.
• Mentorship:Support the growth of junior team members by sharing knowledge reviewing code and guiding them in data engineering best practices and project work.

Who Were Looking For:

• Programming Skills (Key Requirement)
  Strong programming skills inPython(e.g. pandas PySpark SQLAlchemy airflow-like tools). Ability to write clean maintainable and testable code is essential. Experience with other programming languages is a plus.
• Demonstrated hands-on experience with buildingdata pipelines ideally usingSpark(PySpark) or similar distributed processing frameworks.
• Designing and implementing ETL/ELT workflows.
• Working with large datasets and complex data transformations.
• Database Expertise:Proficiency inSQL databases(designing schemas writing complex queries optimization).
• Cloud & Big Data (Nice to Have / Advantage):Experience with cloud data platforms (preferablyAzure: Synapse Databricks Data Factory Azure SQL Data Lake) or similar services on AWS/GCP.
• Professional Background:At least2 years of relevant professional experiencein data engineering BI engineering or similar data-focused roles.
• Analytical Thinking:Strong analytical mindset with the ability to understand complex data landscapes debug data issues and design logical efficient data flows.
• Language Skills:English atB2 level or higher.

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