Top Data Engineer Jobs in Poland

Seeking a Senior Data Support Engineer with 5-6 years of experience in Data Engineering, specifically utilizing Snowflake, Python, SQL, and AWS for data manipulation, warehousing, and testing processes.

Build and operate large-scale data pipelines and orchestration for generative AI research, focusing on video/audio datasets, feature extraction, labeling, and dataset quality to improve model performance.

Design and develop cloud-native backend services in Rust to process terabytes of data daily. Build and operate data-intensive microservices on AWS and Kubernetes, integrate with distributed databases and messaging systems (Kafka/RabbitMQ), and collaborate across teams to deliver observability features.

Lead a data engineering team, develop AI self-service portal and data pipelines, interface with internal teams, and ensure software engineering best practices.

The Senior Data Engineer will design and maintain scalable data pipelines, ensuring secure and timely data delivery for analytics and AI applications.

The Senior Data Science Engineer will architect software solutions, drive AI research, design AI systems, and mentor junior engineers while focusing on innovative solutions in the travel domain.

Lead technical delivery of data-driven solutions across the data lake and data center. Design, build, and document full-stack data systems, apply Master Data Management, improve data models, mentor engineers, collaborate with business stakeholders, and drive long-term data infrastructure strategy and operational efficiency.

The Principal Data Science Engineer will leverage statistical analysis and machine learning to optimize airline revenue performance and customer experience, collaborating with teams to develop data models and algorithms, and support product development cycles.

Lead design and delivery of modular, automated, event-driven data pipelines and data models on Google Cloud. Build resilient, testable ETL/ELT processes, embed quality-by-design, coach junior engineers, collaborate with engineering and QA, and ensure data governance and privacy alignment while driving DevOps and IaC adoption across the Data function.

The Principal Data Platform Engineer will design and implement scalable data platforms, lead engineering initiatives, mentor teams, and ensure data governance and discoverability.

The Senior Data Platform Engineer at Moderna is responsible for advancing data platform capabilities, implementing robust data services on AWS, enhancing platform functionalities, and collaborating with different teams to ensure secure data access and management.

The Senior Data Engineer will develop and maintain scalable data pipelines, model data for analytics, optimize performance, ensure quality, and lead engineering standards.

As a Senior Software Engineer, you'll design and build scalable market data systems, improve API architecture, and enhance overall system performance, contributing to Alpaca's infrastructure for trading financial assets.

Operate and support Kafka clusters (MSK and on-prem), manage Kafka Connect ecosystem (Debezium, KSQL, Schema Registry), troubleshoot producers/consumers, implement HA/backup strategies, automate provisioning (IaC/scripting), monitor and tune cluster performance, support incident response and on-call, and document operational procedures.

Maintain and expand a high-scale data platform: build and run data pipelines, implement DataOps, collaborate with analytics teams, ensure data quality and security, and design enterprise data models.

Design, build, and maintain ETL/ELT pipelines and semantic data models in Microsoft Fabric and Azure. Develop complex SQL solutions and DAX logic for Power BI reporting. Implement CI/CD and DevOps practices, monitor and optimize pipeline performance, ensure data quality and governance, and collaborate with international stakeholders on data architecture and analytics solutions.

Design and implement streaming and batch data pipelines, perform feature engineering and data exploration, build Gold data layer and KPIs, collaborate with business teams to standardize sensor data, and establish production-ready engineering practices (CI/CD, IaC, testing, documentation) for an anomaly detection PoC.

Design and build data architectures and pipelines, optimize data management, collaborate with AI teams, and implement best engineering practices.

As a Senior Data Engineer, you'll build and manage data pipelines, ensure data quality, and collaborate across teams, contributing to the Data Platform's growth.

Design and maintain data pipelines, ensure data reliability, structure data systems, and manage software development processes while collaborating in a team.

Design and build scalable real-time and batch data pipelines for programmatic ad data (bid streams, impressions, revenue). Ingest and normalize SSP/DSP/GAM data, implement ETL/ELT workflows, build and maintain data warehouses/lakes, enable auction analytics and yield optimization models, ensure data quality and privacy compliance, and support BI/analytics teams.

Design and build scalable real-time and batch data pipelines for programmatic advertising, ingesting and normalizing SSP/DSP/GAM data, maintaining warehouses, implementing ETL/ELT, ensuring data quality and privacy compliance, and supporting analytics and BI teams.

Lead design and delivery of cloud-native, AI-ready data platforms and near-real-time ingestion pipelines. Optimize Spark/PySpark code, enforce lakehouse and medallion standards, implement observability, CI/CD, and self-service tooling, and collaborate with Data Science/ML teams to productionize pipelines.

Lead design and delivery of cloud-native, AI-ready data platforms and near-real-time ingestion pipelines. Optimize Spark/PySpark code, define platform standards (lakehouse, medallion), enforce data quality/observability, drive CI/CD and IaC adoption, and enable self-service tooling while collaborating with ML, data science, and product teams.

Develop and own core product components for tape storage and ransomware detection. Design and implement business logic, add features, work with databases, contribute to architecture, and solve low-level multithreading and performance challenges.