Hybrid Data Engineer - KDB+ Low-Latency Analytics

GFT Technologies Poland; Kraków; Hybrid; PLN 120,000 - 180,000; Full time

Benefits offered by this job

Hybrid work in Kraków

Medical, sport, lunch subsidy, life保险

Job summary

GFT Technologies Poland is seeking a data-focused engineer to design and optimize high-throughput analytics on large time-series data in a hybrid Kraków-office setup. You will develop KDB+ processes, APIs, and analytics layers for AI/ML models, working closely with traders, quants, and product owners to deliver scalable, low-latency solutions.

The role emphasizes production-grade data processing, collaboration with data engineering teams, and ensuring resilience and compliance across platforms.

Qualifications

• Hands-on experience building data processing solutions for high-volume datasets.
• Experience with low-latency, high-throughput environments is essential.
• Ability to translate business requirements into technical solutions for traders and quant teams.

Responsibilities

• Develop and optimize KDB+ processes and queries for low-latency environments.
• Build analytics and aggregation layers for downstream applications and AI/ML models.
• Expose data and analytics through APIs and services for stakeholders.
• Collaborate with AI and data engineering teams for structured datasets for models.
• Implement monitoring, alerting, and capacity management across KDB+.

Skills

Data processing

Low-latency

Large-scale datasets

Trading domain knowledge

Problem-solving

Collaboration

Code reviews

Tools

KDB+

Python

Bash

Unix/Linux

Git

CI/CD pipelines

Time-series databases

Job description

GFT Technologies Poland is seeking a data-focused engineer to design and optimize high-throughput analytics on large time-series data in a hybrid Kraków-office setup. You will develop KDB+ processes, APIs, and analytics layers for AI/ML models, working closely with traders, quants, and product owners to deliver scalable, low-latency solutions.

The role emphasizes production-grade data processing, collaboration with data engineering teams, and ensuring resilience and compliance across platforms.