Junior Data Engineer

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.

PwC SDC Lviv, opened in 2018, is part of this global space. It is a place where technology is combined with team spirit, and ambitious ideas find their embodiment in real projects for Central and Eastern Europe.

What do we guarantee?

• Work format: Remote or in a comfortable office in Lviv - you choose.
• Development: Personal development plan, mentoring, English and Polish language courses.
• Stability: Official employment from day one, annual review of salary and career prospects.
• Corporate culture: Events that unite the team and a space where everyone can be themselves.

We are currently looking for a Junior Data Engineer with Python and SQL skills to join our growing data team.

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.
• 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 We're Looking For:

• Programming Skills (Key Requirement)
  Strong programming skills in Python (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 building data pipelines, ideally using Spark (PySpark) or similar distributed processing frameworks.
• Designing and implementing ETL/ELT workflows.
• Working with large datasets and complex data transformations.
• Database Expertise: Proficiency in SQL databases (designing schemas, writing complex queries, optimization). 
• Cloud & Big Data (Nice to Have / Advantage): Experience with cloud data platforms (preferably Azure: Synapse, Databricks, Data Factory, Azure SQL, Data Lake) or similar services on AWS/GCP.
• Professional Background: At least 1-2 years of relevant professional experience in 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 at B2 level or higher.
  
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