Top Data Engineer Jobs in Poland

Designs and operates batch and streaming data pipelines on Databricks and Spark for fintech lending use cases. Owns feature-store reliability, point-in-time correctness, data modeling, governance, observability, incident response, and data-quality controls. Develops agentic workflows with tools, evaluations, state, and guardrails around feature creation and monitoring. Requires strong Python, SQL, Spark, lakehouse, orchestration, and financial data engineering expertise.

Design and own the feature-store architecture supporting credit decisioning, including schemas, point-in-time correctness, freshness SLAs, lineage, data quality, observability, and drift monitoring. Build reference data-engineering patterns and lead complex cross-domain initiatives in regulated environments. Architect agentic harnesses and loop or graph workflows with reliable context, tools, evaluations, guardrails, and cost controls. Serve as a hands-on technical authority who establishes standards and mentors through influence.

Lead data engineering for Finance by designing dbt dimensional models, building Python or Go ETL pipelines, and loading data into BigQuery on Google Cloud. Own data quality, reconciliation, observability, documentation, and production reliability. Lead multi-sprint initiatives, partner with Finance and BI stakeholders, review code, mentor engineers, and use AI-assisted development tools responsibly. The role focuses on trusted financial reporting data, including billing, subscriptions, payments, revenue, and related metrics.

Build secure production services on Google Cloud from prototypes, develop Python APIs and data pipelines, integrate business systems with canonical data layers, automate reporting, and own deployment, monitoring, security, documentation, and stakeholder communication. The role requires strong cloud, data engineering, full-stack, and service ownership skills, with emphasis on autonomous delivery and secure software practices.

Build end-to-end data applications, APIs, internal tools, and user interfaces using AI-assisted development. Work across Python backends, React frontends, data models, Databricks, dbt, pipelines, cloud infrastructure, testing, security, and performance. Review AI-generated code, establish guardrails, improve engineering workflows, and help shape application architecture and technical direction.

Develop and deploy scalable, production-grade machine learning and AI solutions for financial research. Build LLM- and NLP-based applications, apply modern deep learning architectures, and collaborate with senior engineers on reliable AI products. Responsibilities include software development with Python, database integration, containerization with Docker, and potentially developing RAG pipelines, vector database solutions, and fine-tuned LLMs.

Own and lead the big data infrastructure platform, including deploying, upgrading, and operating HDFS, object storage, Iceberg, monitoring, and alerting. Drive infrastructure modernization, migrate Iceberg catalogs, implement multi-tenant quotas, improve observability, and support large-scale data architecture changes. Collaborate with a parallel infrastructure engineer, share solutions, provide coverage, and influence platform architecture.

Design and deliver cloud-native data platforms, scalable big data pipelines, lakehouses, warehouses, transformation frameworks, BI solutions, and ML-enabled data workflows. Lead client-facing pre-sales engagements, technical workshops, proof-of-concepts, executive conversations, and enterprise data modernization initiatives. Build reusable architectural IP, influence senior stakeholders, and translate ambiguous business challenges into production-ready solutions using AWS and modern data technologies.

Develop and maintain scalable AWS data engineering platforms and reusable Python modules. Build object-oriented data processing solutions, debug pipelines across Step Functions, Glue, Athena, and Lambda, and apply modern engineering practices including testing, CI/CD, and version control. Collaborate with agile teams to deliver reliable solutions across countries and business environments.

Design and implement Azure-based data processing systems, scalable data pipelines, data warehouses, data lakes, and ETL workflows. Transform and optimize structured and unstructured data, tune queries, improve storage and retrieval performance, and resolve system bottlenecks. Collaborate with data scientists, analysts, and stakeholders to deliver technical solutions while working within Agile methodologies.

Administer and maintain Linux server and embedded environments, including configurations, networking, permissions, services, and troubleshooting. Develop, test, and maintain C++ and Python software for data logging, processing, and visualization. Diagnose system, hardware, network, and application issues; create technical documentation; support product tools; and collaborate with software, hardware, and integration teams to improve system quality and efficiency.

Designs, builds, and supports GCP data ecosystems, including cloud storage, processing, warehouses, data lakes, pipelines, and integrations. Responsibilities include requirements gathering, data modeling, performance optimization, troubleshooting, monitoring, root cause analysis, and client collaboration. The role also mentors junior data engineers and contributes to ETL/ELT, orchestration, transformation, and database management initiatives.

Design, implement, and maintain scalable streaming and batch data pipelines using Apache Pinot, Iceberg, Flink, and Spark. Improve pipeline performance, reliability, observability, and data quality while participating in software development, testing, incident resolution, and postmortems. Collaborate with engineering, product, and data teams, use AI coding tools, and continuously develop expertise in production data systems.

Lead the architecture and development of scalable Databricks data solutions for global clients. Build PySpark pipelines, migrate legacy warehouses to Delta Lake, optimize Spark performance and costs, implement governance with Unity Catalog, and establish code quality and CI/CD standards. Advise stakeholders on data strategy, mentor engineers, and deliver modern Lakehouse architectures using cloud platforms and contemporary orchestration tools.

Design, build, and maintain AWS-based lakehouse solutions and medallion architecture using Iceberg and Glue. Implement CDC and streaming ingestion with Debezium and Kafka. Develop PySpark batch/distributed jobs on EMR/Glue, orchestrate workflows with Airflow, ensure data quality/governance, and collaborate on infrastructure and IaC practices to deliver reliable business-ready datasets.

Design and implement data-driven applications and end-to-end data systems for clients. Responsibilities span cloud engineering, infrastructure, data engineering, ETL, machine learning, and data science. The role involves evaluating and presenting results, applying software engineering best practices, collaborating with data scientists and stakeholders, and delivering solutions through agile, iterative development.

Design and implement scalable backend solutions for collecting and managing large volumes of data across Veeam’s cloud, SaaS, virtualization, backup, and replication products. Develop features, own major components, contribute to architecture, define requirements with product management, and estimate implementation effort, costs, and capacity. The role requires strong C# and .NET expertise, software architecture experience, problem-solving skills, and effective communication in English.

Build and improve Modash’s large-scale creator search system across retrieval, indexing, ranking, embeddings, relevance, and low-latency serving. Own initiatives end to end, from requirements and architecture through implementation, production deployment, measurement, and iteration. Develop multimodal search capabilities using image, video, text, audio, LLMs, and vector search. Collaborate with data, product, customers, and leadership while balancing quality, cost, latency, and reliability.

Build and operate resilient, large-scale backend and data collection systems managing 400M+ creator profiles and billions of data points. Design distributed services that handle API instability, rate limits, outages, and partial failures; optimize collection economics; and own architecture, implementation, deployment, observability, and production operations. Collaborate with data and product teams to improve freshness, reliability, coverage, and system performance.

Build and maintain a centralized data platform, including SQL Server warehouses, SSIS processes, scalable SQL/Python/Spark pipelines, data integrations, CI/CD pipelines, and Infrastructure as Code. The role also involves improving legacy code, ensuring data quality and reliability, contributing to architecture and engineering standards, conducting code reviews, documenting solutions, and collaborating with team members.

Design and optimize scalable GCP cloud architectures, data platforms, ETL/ELT pipelines, and analytical workloads. Implement Terraform-based infrastructure, CI/CD pipelines, and automation while improving performance, reliability, security, and cost efficiency. Troubleshoot complex issues, establish engineering standards, support cloud operations, and provide technical leadership and mentoring across engineering, data, and architecture teams.

As a Principal Big Data Engineer, you will design scalable solutions, develop data pipelines, create features, and collaborate with various teams to enhance Akamai's data platform.

Build and operate production-grade data pipelines and reusable data products for economic policy and research. Manage data acquisition, storage, versioning, metadata, lineage, validation, and access across structured and unstructured sources. Support economic time-series requirements such as revisions, vintages, seasonal adjustment, and frequency conversion. Develop LLM-based extraction validation frameworks, enable governed AI workflows, connect datasets to analytical and BI tools, and guide junior engineers while collaborating with economists and technical teams.

Build and maintain scalable GCP data pipelines, BigQuery data models, enterprise semantic layers, and CI/CD workflows for a modernized marketing data platform. Rebuild legacy pipelines, support DataHub adoption, enforce data quality, optimize performance and costs, and enable secure multi-brand marketing reporting. Collaborate with product, Martech, analytics, and data engineering teams.

Develop and maintain Python-based Splunk services and add-ons for large-scale, real-time data ingestion and processing. Participate in requirements, design, development, testing, automation, deployment, code reviews, troubleshooting, and on-call support. Build cloud-based backend solutions using AWS, Azure, or GCP, with LLMs, Generative AI, agentic architecture, and RAG. Improve system reliability, diagnose production issues, and create maintainable, documented software.