PwC

AI Security Consulting

DataEngineer(m/f)

€2k+ bratislava, bratislava region, slovakia; košice, košice region, slovakia FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Entry candidates.

The Brief

“Data Engineer (m/f) at PwC. Skills: Python, Data pipelines, ETL/ELT, SQL. Design data pipelines. Implement data pipelines”

What You'll Achieve.

Build data foundations behind impactful AI solutions; Power real impact; Ensure architecture supports GenAI; Ensure architecture supports dashboards; Ensure architecture supports interfaces; Ensure architecture supports anomaly detection; Ensure architecture supports optimization; Ensure datasets are clean; Ensure datasets are consistent; Ensure datasets are production-ready; Ensure performance; Ensure cost efficiency; Ensure reliability; Ensure scalability; Adherence to SLAs; Provide expert guidance on data architecture; Provide expert guidance on best practices

Industry & Context.

AI Security Consulting
Problems you'll solve

Full-solver flexibility; Automation; Integration work; Analytical thinking; Debug data issues; Design logical data flows; Design efficient data flows

What They're Looking For.

Must Have

Strong programming skills in Python, pandas, PySpark, SQLAlchemy, airflow-like tools, Clean, maintainable, and testable code, Hands-on experience building data pipelines, Spark (PySpark), Distributed processing frameworks, ETL/ELT workflows, SQL databases, Designing schemas, Writing complex queries, Optimization, 2–3 years of professional experience in data engineering, BI engineering, or similar data-focused roles, Strong analytical mindset, Debug data issues, Logical, efficient data flows, English at B2 level or higher

Nice to Have

Other programming languages, Data preprocessing needs for ML models, NoSQL databases, MongoDB, Cassandra, Cosmos DB, Cloud data platforms, Azure: Synapse, Databricks, Data Factory, Azure SQL, Data Lake, AWS/GCP

What You'll Do.

Design data pipelines

Implement data pipelines

Maintain data pipelines

Build ETL/ELT processes

Build data warehouses

Implement preprocessing steps

Implement feature engineering steps

Optimize data processing jobs

Monitor pipelines in production

Implement data quality checks

Implement validation frameworks

Implement governance standards

How You'll Work.

Team & Collaboration

Engage with global teams; Work hand-in-hand with data scientists; Work hand-in-hand with ML engineers; Collaborate with clients; Share knowledge with junior team members; Code reviews; Mentorship; Hands-on guidance

Communication Scope

Understand business needs; Provide expert guidance

Full Job Description

**Job Description & Summary** **Location:****** Bratislava, Košice (Hybrid) — Engage with global teams across Western Europe and the USA **Team:** Advanced Technology Solutions (ATS) - AI Security Consulting at PwC 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**. **Python** is the core skill we expect. Depending on your strengths, you may focus on **pipelines** , **platforms** , **SQL** , or 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:** * **Build the Backbone of AI Solutions** : Design, implement, and maintain scalable **data pipelines** and **ETL/ELT processes** using **Python** and **Spark** (PySpark). You'll ingest, transform, and deliver data from diverse sources — from industrial sensor streams and satellite imagery to unstructured pharmaceutical documents — into **analytics and ML platforms** that power real impact. * **Design Data That Scales** : Create and optimize **data models**(star/snowflake schemas), build**data warehouses** and**data lakes** , and ensure your architecture supports everything from GenAI-powered dashboards and natural language interfaces to anomaly detection in financial systems and real-time industrial optimization. * **Enable Machine Learning: **Work hand-in-hand with **data scientists** and**ML engineers**. Implement robust **preprocessing** and **feature engineering** steps so that datasets feeding NLP models, computer vision pipelines, classification systems, and predictive analytics are clean, consistent, and production-ready. * **Make It Fast, Keep It Rel

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