PwC

DataEngineer

lviv, lviv, ukraine FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid+ candidates.

The Brief

“Data Engineer at PwC. Skills: Python, Spark, SQL, Data pipelines. Design data pipelines. Implement data pipelines”

Industry & Context.

Problems you'll solve

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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