Top Data Engineer Jobs in Krakow

Designs and maintains scalable data ingestion, transformation, ETL/ELT, and data quality pipelines using Databricks and cloud platforms. Builds metadata-driven frameworks, supports Bronze-to-Gold data flows, implements testing and reconciliation, and contributes to monitoring, CI/CD, migrations, performance optimization, and operational support. Collaborates with data, BI, platform, architecture, and client teams while supporting enterprise data products and knowledge transfer.

Build and maintain scalable production data pipelines, machine learning data pipelines, and data transformation frameworks for financial crime detection solutions. Collaborate with product, R&D, data, analytics, customers, and partners; optimize large-scale data architectures; support feature engineering and root cause analysis; and train customer teams. The role requires customer-facing collaboration, English and Spanish fluency, and domestic and international travel when needed.

Design and optimize Snowflake data models and dbt transformation workflows, modernize Oracle ETL processes, build Python automation, and support scalable data architecture. The role involves troubleshooting data quality and performance issues, contributing to DevOps and deployment activities, owning technical deliverables, and participating in client-facing discussions and solution design.

Design, develop, and maintain scalable Databricks data pipelines and ETL/ELT workflows using Python and SQL. Optimize cloud data solutions for performance, reliability, and cost efficiency across Azure, AWS, or GCP. Build data models, tables, and integrations; monitor and troubleshoot pipelines; collaborate with analysts, scientists, and cross-functional teams; and support AI, analytics, and MLOps initiatives.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Designs, builds, and maintains scalable data and machine learning infrastructure, pipelines, databases, integrations, self-service tools, and MLOps workflows. The role includes platform expansion, observability, security, data quality, performance optimization, infrastructure as code, CI/CD, documentation, and collaboration with data scientists, analysts, and stakeholders. Strong experience with cloud platforms, Kubernetes, containerization, databases, big data, orchestration, and AI-assisted development is required.

Designs and maintains CI/CD pipelines, reusable delivery templates, deployment automation, artifact distribution, Docker workflows, and Kubernetes environments. Automates platform engineering activities, supports Python packaging migrations from Poetry or setuptools to UV, and improves software delivery governance, traceability, and repeatability. The role also collaborates with engineering teams on DevOps practices and maintains technical documentation, with opportunities to support GCP-based enterprise data platforms.

Build large-scale data pipelines and infrastructure for frontier AI model training. Develop data-processing models such as classifiers, quality filters, and labeling systems; design deduplication, quality scoring, labeling, and augmentation strategies; and create reliable tooling for researchers to explore and train on massive datasets. The role also evaluates how data quality and composition affect model outcomes, with web crawler experience as a bonus.

Staff Software Engineer responsible for designing, building, and operating distributed market data systems. The role focuses on real-time blockchain data ingestion, streaming, normalization, storage, APIs, reliability, data correctness, observability, incident recovery, and production support. The engineer will lead technical decisions, mentor teammates, translate ambiguous requirements into designs, and use AI-assisted development throughout the software lifecycle.

Build and maintain high-performance data lakehouse infrastructure using Java and Rust. Develop reliable Apache Iceberg maintenance workflows, including compaction, snapshot expiry, manifest rewriting, and orphan file cleanup. Ensure data integrity under concurrency, retries, and failures. Maintain a Rust-based SQL query router, develop connectors, review open-source contributions, support production deployments, and participate in on-call debugging.

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.

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.

Build and maintain data pipelines and infrastructure for analytics, experimentation, and company metrics. Collaborate with machine learning engineers on training, quality assessment, and model release automation. Improve data delivery processes, automate manual workflows, and develop scalable data models. The role requires strong Apache Spark and Python expertise, SQL proficiency, Git experience, and familiarity with cloud data platforms and Kubernetes.

Build and maintain data pipelines, infrastructure, data models, and automation for analytics and machine learning workflows. Collaborate with ML engineers on model training, quality assessment, and releases. Improve internal data delivery processes and enable efficient access to large-scale organizational data.

Own and evolve a production lakehouse supporting millions of daily financial events. Design bronze, silver, and gold layers; build CDC streaming pipelines; optimize storage and queries; manage retention, archival, reconciliation, and data correctness. Establish governance through cataloging, lineage, access controls, masking, encryption, and audit trails. Monitor ingestion health, anomalies, and cloud costs while supporting reliable, traceable data for Analytics, Finance, and Regulatory teams.

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 Databricks data pipelines, standardize and migrate legacy pipelines, develop dbt and Python transformations, onboard regional datasets, optimize performance and resource usage, and collaborate with architects and analytics teams. The role also contributes to future Data Science and MLOps foundations while supporting cloud-based data platforms and data modeling practices.

Build and maintain AWS-based data pipelines and lifecycles (Glue, DMS, Redshift, S3). Develop production data transformations and CDC using Scala and Python, automate CI/CD with Bash, optimize Spark jobs (partitioning, Parquet, broadcast joins), and deliver reliable, idempotent pipelines while collaborating remotely with cross-functional teams.

Design, build, test, deploy, and operate scalable Azure and Databricks data pipelines and analytical data products. Refactor pipelines for reliability, performance, observability, and maintainability; diagnose incidents; and implement SRE, DataOps, CI/CD, testing, monitoring, governance, and security practices. Collaborate with data, architecture, product, and engineering teams to onboard workloads, translate reporting needs into technical solutions, document data flows, and share engineering 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.

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.

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.