Top Data Engineer Jobs in Warsaw

Execute large-scale data platform migrations to Snowflake, adapting approximately 1,000 datasets and existing pipelines. Develop, test, schedule, and monitor Airflow workflows while supporting migrations from Airflow 2 to 3, Confluent regions, and legacy formats to Iceberg. Ensure data quality, reliability, and performance across AWS, Spark, Kafka, and Snowflake environments while collaborating with data platform teams on modernization initiatives.

Build and optimize scalable data pipelines and products supporting marketing budget decisions and attribution. Use Python, SQL, and Airflow while ensuring data quality through validation, monitoring, and documentation. Collaborate with product and marketing stakeholders, participate in code reviews, investigate data anomalies, and explain insights to nontechnical audiences. Work as an independent B2B contractor based in Poland.

Develop and maintain scalable Credit Risk data pipelines and products using SQL, Python, Databricks, and cloud platforms. Modernize legacy SAS processes, onboard source data, and support migration to cloud architecture. Responsibilities include data transformation, validation, reconciliation, governance, documentation, troubleshooting, automation, and analysis across complex datasets. The role collaborates with business, technology, and data governance teams to deliver trusted data solutions for regulatory, forecasting, modeling, and reporting needs.

Build, maintain, and optimize ETL processes, data pipelines, and Teradata data warehouse solutions for banking applications, regulatory reporting, risk, and analytics. Responsibilities include data validation, profiling, reconciliation, performance tuning, data quality, compliance, governance, metadata management, and collaboration with business and technology stakeholders.

Build and optimize data models, integrate data from databases, APIs, streaming platforms, and files, and maintain reliable, cost-efficient pipelines and storage. The role involves troubleshooting data quality issues, reducing technical debt, and partnering with business stakeholders to translate requirements into effective data solutions. Strong SQL, Python, cloud platform knowledge, and fluency in Polish and English are required. The position is based in Warsaw with three office days per week.

Build and optimize data models, integrate data from databases, APIs, streaming platforms, and files, and monitor pipeline performance, reliability, and costs. Troubleshoot data quality issues, reduce technical debt, and collaborate directly with business stakeholders to translate requirements into effective data solutions.

Build and operate production data pipelines, dbt models, Airflow DAGs, and AWS infrastructure as code. Own ingestion, transformation, orchestration, monitoring, data quality, observability, and CI/CD. Support AI-ready data initiatives and Redshift access, collaborate with stakeholders, and provide end-to-end ownership from requirements through troubleshooting. The role requires strong Python, SQL, AWS, analytical data modeling, infrastructure-as-code, and autonomous problem-solving skills.

Designs and builds production-grade AI applications natively on Snowflake using Cortex Analyst, Cortex Search, Snowflake Agents, semantic models, Python, and SQL. Develops secure, performant natural-language querying, retrieval, text-to-SQL, and agentic workflows over governed data. Owns delivery of a technical workstream, collaborates with data scientists, engineers, clients, and stakeholders, and supports enterprise GenAI and analytics solutions. Snowflake implementation experience is mandatory; regulated-industry experience is advantageous.

Develop and maintain reliable, scalable data pipelines and infrastructure. Responsibilities include data ingestion, transformation, modeling, ETL, quality validation, governance, storage optimization, and delivery for analytics and reporting. Collaborate with data scientists, analysts, and stakeholders to meet business data needs, troubleshoot issues, document systems, and apply modern data engineering practices.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

Design, build, and maintain scalable data pipelines, models, and backend data infrastructure. Develop dbt transformation workflows on Snowflake or Databricks, manage orchestration, and create internal data applications with tools such as Streamlit. Translate product requirements into technical solutions, collaborate across engineering and analytics teams, and use GenAI tools to accelerate development from design through deployment and continuous improvement.

Own end-to-end data readiness across ingestion, validation, transformation, aggregation, extraction, and delivery workflows. Develop and optimize Python, PySpark, SQL, Databricks, ADF, and Delta Lake solutions; investigate data-quality and reconciliation issues; coordinate validation with stakeholders and providers; resolve complex pipeline and platform incidents; review engineering work; guide junior engineers; and maintain documentation, contracts, runbooks, and deployment procedures.

Design and develop scalable data ingestion pipelines, backend services, REST APIs, and data-processing systems using Python and Django. Build reliable integrations from file and API sources into cloud data platforms, with strong emphasis on validation, data quality, error handling, testing, scalability, and production readiness. Collaborate with stakeholders, maintain CI/CD environments and documentation, use workflow orchestration tools such as Airflow, and contribute to cloud-based data engineering projects.

Supports enterprise data pipelines through monitoring, ingestion, validation, troubleshooting, testing, documentation, and minor enhancements. Works with Python, PySpark, SQL, APIs, files, cloud data platforms, and data quality processes under senior guidance. Contributes to source onboarding, operational support, Git workflows, code reviews, and deployment practices in a six-month engagement.

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.

Designs, builds, maintains, and optimizes enterprise data platforms, pipelines, warehouses, models, and integration frameworks supporting Power BI, analytics, AI, and operational reporting. The role develops Azure-based ETL/ELT solutions, improves data quality and reliability, establishes governance and standardized metrics, and collaborates with BI, IT, automation, and AI teams. It is a hybrid position in Katowice, Poland.

Build and maintain big data solutions for fraud, compliance, and financial crime use cases. Manage, transform, and cleanse high-volume data using Spark, Hadoop, Scala, Data Fusion, and Elasticsearch. Collaborate with stakeholders, analysts, technical leads, project managers, and architects in an Agile Scrum environment to deliver scalable technical solutions and production data pipelines.

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.

Migrate legacy Oracle SQL and PL/SQL business logic to BigQuery and dbt on GCP. Develop, test, optimize, and maintain cloud data processing solutions; validate consistency between legacy and new implementations; implement customer record grouping and identification; and prepare for Golden Record capabilities. Collaborate with architects, analysts, data owners, and platform teams while maintaining technical documentation and using AI tools to accelerate modernization.

Develop and maintain GCP lakehouse Gold and Platinum layers, build dbt and PySpark transformations, and design Airflow pipelines. Optimize BigQuery performance and costs, manage Terraform infrastructure, implement GitHub Actions CI/CD, and support IAM, DLP, Dataplex, data governance, metadata, and lineage practices. Collaborate with stakeholders and engineering teams to deliver reliable, secure data platforms while contributing to code reviews and technical consulting.

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 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.

Design and deliver enterprise-scale Snowflake data pipelines, ingestion processes, transformations, curated datasets, data quality checks, monitoring, documentation, and operational handover for a regulated financial institution. Collaborate with governance, architecture, client, and engineering teams on metadata, lineage, access controls, deployment, stabilization, tuning, and knowledge transfer. The one-year engagement requires relocation from Poland to the Middle East.