Top Data Engineer Jobs in Warsaw

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.

Develop and maintain unified observability across Kubernetes, databases, Trino, Hadoop, and Apache Ozone. Build DORA metrics collection and service health dashboards, identify monitoring gaps, optimize Alertmanager rules, reduce alert fatigue, and provide centralized SRE expertise. The role requires strong Prometheus, Grafana, Alertmanager, PromQL, reliability metrics, incident analysis, and distributed-systems knowledge.

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.

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.

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 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 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 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.

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.

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.

Build and maintain Python ETL pipelines, data warehouses, and data lakes supporting marketing analytics and personalized marketing programs. Integrate mobile marketing data, automate scalable and cost-efficient workflows, and ensure data quality, validation, governance, and performance. Collaborate with marketing and studio teams to deliver data-driven solutions. The role uses Snowflake or Firebolt, Airflow, SQL, distributed computing, and modern AI development tools.

Designs, develops, deploys, and maintains big data engineering solutions processing billions of healthcare records. Builds and optimizes ETL pipelines in Databricks using Spark, Azure, Delta, and Parquet; performs data profiling, integration, quality, and warehousing activities; and contributes to system design, testing, code reviews, and technical standards. Provides technical guidance and mentoring to junior and mid-level engineers while supporting Agile delivery and production operations.

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.

Design and develop scalable full-stack applications, MVPs, prototypes, backend services, and modern frontend solutions. Integrate APIs, databases, and cloud services while collaborating with product owners, architects, AI engineers, and business stakeholders. Translate innovation concepts into software, support emerging technology experimentation, and follow engineering best practices including testing, code reviews, documentation, and version control. The role supports multiple client-facing projects and requires adaptability and travel as needed.

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.

The Senior Data Engineer will design and maintain data systems, build data pipelines, optimize ETL processes, and ensure data quality, collaborating with various teams to facilitate data-driven decision making.

Designs, develops, and maintains scalable Azure and on-premises data pipelines, enterprise data products, ETL/ELT processes, and data warehouses. Uses Azure Data Factory, Synapse, ADLS, Databricks, SQL Server, SSIS, SQL, and Python. Builds CI/CD pipelines with Azure DevOps and GitHub, optimizes performance and data quality, troubleshoots production issues, documents solutions, and collaborates with stakeholders in Agile teams.

Design and build production-ready AI solutions, including RAG pipelines, autonomous agents, and MCP integrations. Shape AI architecture, infrastructure, scalability, and reliability while integrating enterprise data. Participate in Scrum planning, conduct code reviews, and maintain documentation and testability. The role requires deep Python expertise, distributed systems knowledge, container orchestration, databases, caching, observability, and LLM orchestration frameworks.

Lead the technical strategy, architecture, and delivery of Cisco’s Partner Hub Analytics ecosystem. Design scalable data pipelines using Pinot, Iceberg, Flink, and Spark; establish standards for data quality, observability, governance, performance, and reliability; guide engineering teams; mentor engineers; and collaborate with product and business leaders on high-scale analytical solutions.

Design and optimize databases and data processing systems, collaborate with developers, gather user requirements, and provide user support.