Senior / Tech Lead Data Engineer

Level

Department

Python

Type

Full Time

Project

Bridgewise

Locations:

Poland

Remote

Job Details

Posted on:

May 22, 2025

Job ID:

428

About the Company

Established in 2004, ALLSTARSIT was founded with a clear vision: to enhance the landscape of global IT employment by bridging the gap between companies and skilled professionals. The core belief was that assembling a team shouldn't be hindered by geographical constraints. Fast forward to the present day, ALLSTARSIT stands as an international outstaffing service provider committed to change the way businesses recruit, compensate, and oversee top talent worldwide.

With operational hubs scattered across Europe, Asia, and LATAM, and its headquarters situated in San Francisco, US, the company boasts a workforce of over 1,000 adept professionals. Spanning across more than 20 countries, ALLSTARSIT offers a diverse range of skilled employees across various verticals, including AI, cybersecurity, healthcare, fintech, telecom, media, and so on.

About the Project

Bridgewise is building advanced data platforms that power real-time analytics and decision-making processes for global clients. With a focus on scalability, performance, and cloud-native architecture, the company delivers end-to-end data engineering solutions. The team works with cutting-edge technologies such as AWS EMR, Glue, Spark, and Airflow, and leverages Python and PySpark for efficient data processing.

Specialization

Headquarters

Years on the market

Team size and structure

Current technology stack

Required skills:

Bachelor’s Degree in Computer Science or a related field
5+ years in data engineering or architecture roles, with leadership experience
Advanced Python and PySpark skills for ETL and data transformation
Strong hands-on experience with AWS cloud ecosystem, especially EMR and Glue
Deep understanding of Apache Spark, including performance optimization
Familiarity with orchestration tools like Airflow
Solid knowledge of OOD (Object-Oriented Design) and SOLID principles

Must-have confirmations:
✅ Practical experience with Spark job performance optimization
✅ Understanding of SOLID principles and object-oriented design
✅ Familiarity with at least 4 of the following PySpark topics: Window functions, Broadcast join, Sort & merge join, Watermark, UDF (User-Defined Functions), Lazy computation

Scope of work:

Design and implement ETL pipelines for ingesting and transforming data from multiple sources
Collaborate with cross-functional teams to ensure reliable deployment and integration of data workflows
Lead performance tuning and query optimization for high-efficiency data processing
Model and structure data to support scalable, robust, and maintainable platforms