PwC
DataEngineer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Data Engineer at PwC. Skills: Python, Spark, SQL, Data pipelines. Design data pipelines. Implement data pipelines”
Industry & Context.
Analytical mindset; Understand complex data landscapes; Debug data issues; Design logical data flows; Design efficient data flows
What They're Looking For.
Must Have
Python, SQL databases, 2 years of relevant professional experience
Nice to Have
Azure, AWS, GCP, Spark, PySpark, SQLAlchemy, airflow-like tools, B2 level English
What You'll Do.
Design data pipelines
Implement data pipelines
Maintain data pipelines
Build data warehouses
Manage data warehouses
Monitor data pipelines
Ensure data reliability
Ensure data scalability
Implement data quality checks
Implement validation frameworks
Ensure data compliance
Improve existing solutions
How You'll Work.
Team & Collaboration
Collaborate with data scientists; Collaborate with ML engineers; Work with clients; Support junior team members; Share knowledge; Review code
Communication Scope
Client collaboration; Provide expert guidance
Full Job Description
**Job Description & Summary** **PwC** is a global network of more than **370,000 professionals in 149 countries** that turns challenges into opportunities. We create innovative solutions in audit, consulting, tax and technology, combining knowledge from all over the world. Join PwC’s pioneering data & AI team and help build the data foundations behind impactful AI solutions. We’re growing quickly due to a strong pipeline of client work, and we’re hiring across **multiple seniority levels** — from junior to senior Data Engineers and Data Scientists. Across our projects, **Python is the core skill** we expect. Depending on your strengths, you may focus more on **data engineering** (pipelines, platforms, SQL) or **data 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
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