Top Data Engineer Jobs in Warsaw

Design, build, and optimize Snowflake-based cloud data platforms and scalable ELT/ETL pipelines for financial-services clients. The role involves data integration, modeling, governance, performance and cost optimization, advanced Snowflake capabilities, stakeholder collaboration, technical leadership, mentoring, and delivery of client projects in an Agile environment. The one-year engagement requires relocation to Qatar.

Design, build, test and support batch and streaming data pipelines and curated datasets on a Lakehouse/AI data platform. Implement data modelling, schema evolution, partitioning and data quality controls. Work with stakeholders to deliver production-ready, well-tested, high-performance data products and support operational monitoring and reconciliation.

Design, build, and maintain reliable data pipelines supporting accounting and tax systems. Migrate legacy SQL Server solutions to Databricks and Spark, develop Python REST services and web interfaces, integrate data sources, and create CI/CD and Infrastructure as Code solutions. The role also involves data modeling, workflow orchestration, quality monitoring, architectural decisions, code reviews, documentation, and collaboration with engineering and finance stakeholders under monthly accounting deadlines.

Designs and maintains scalable GCP cloud platforms for migration, self-service, automation, and AI enablement. Responsibilities include Terraform-based infrastructure as code, CI/CD pipelines, cloud-native architecture, observability, governance, security, cost optimization, and platform reliability. The role collaborates with data engineers, architects, and delivery teams while developing reusable engineering patterns and supporting large-scale enterprise cloud transformation.

Designs and maintains production-grade ETL/ELT pipelines and data models using Snowflake, SQL, Python, dbt, and orchestration tools. Responsibilities include Snowflake performance optimization, access management, cloud integration, data lake and lakehouse development, CI/CD, testing, and documentation. The role collaborates with technical and business stakeholders in German and supports enterprise reporting, automation, and AI-ready data solutions.

Design and deliver Azure-based data platforms and pipelines using Databricks, Medallion Architecture, Delta Lake, and dbt. Analyze source systems, data quality, governance, integration requirements, and KPI feasibility across HR, payroll, finance, and operational data. Produce technical designs, standards, estimates, and risk assessments while collaborating with architects, engineers, clients, and business stakeholders through workshops and architecture reviews.

The Data Engineer will build and scale data pipelines, ensure data quality, implement dbt best practices, and empower cross-functional teams with self-service data access.

Designs, builds, and maintains scalable ETL/ELT pipelines, data models, storage solutions, and data workflows, primarily using AWS. Ensures data integrity, governance, security, observability, reliability, and performance across structured and unstructured data systems. Collaborates with product, engineering, analytics, and machine learning teams to support data-driven initiatives and optimize analytical and operational data infrastructure.

Build and optimize scalable data pipelines, lakehouse datasets, and data models using decentralized data ownership and Medallion architecture. Partner with product, engineering, analytics, and business teams to deliver data projects, improve data quality, enable product integrations, promote privacy-by-design, and mentor engineers. Help develop agentic systems for data engineering.

Build and maintain ETL/ELT pipelines, cloud data warehouse models, dashboards, and reports. Ensure data quality, governance, lineage, and reliable metric definitions while translating stakeholder needs into actionable analytics. Support AI-enabled reporting, natural-language querying, anomaly detection, and governed LLM tools. Collaborate with business, IT, and engineering teams to automate reporting and improve self-service data access.

Build and support batch and streaming data pipelines, curated datasets, and Lakehouse platform capabilities. Responsibilities include data modeling, transformation, optimization, quality validation, reconciliation, testing, monitoring, and production support. The role partners with engineers and data consumers to deliver reliable data products for analytics, operational decision-making, and AI use cases.

Build and maintain data warehouse solutions using Snowflake, SQL, dbt, and dimensional modeling. Develop, test, and deploy data models; manage code with Git and Bitbucket; and support CI/CD workflows, containerized deployments, and automation using Python. The role also requires German proficiency and may involve Azure services, orchestration tools, finance data products, and requirements engineering.

Build and maintain data pipelines and ELT processes, implement data quality metrics and controls using dbt and BigQuery, migrate legacy data quality solutions, integrate with GCP services, optimize execution performance, support testing, and document technical and operational processes. Collaborate with data modelers, business analysts, and test engineers on data transformation and monitoring solutions.

Designs and optimizes Google Cloud data platforms, ETL/ELT pipelines, Spark workloads, Airflow DAGs, and event-driven orchestration. Manages Bronze, Silver, and Gold data layers in Google Cloud Storage, implements reprocessing and backfill capabilities, improves metadata-driven tooling, and optimizes performance, cost, observability, and reliability. Collaborates with platform, analytics, and DevOps teams to support production data pipelines.

Designs and operates scalable production data infrastructure for high-volume athlete, sensor, and time-series data. Builds streaming ingestion, graph models, low-latency ML feature systems, evaluation and monitoring infrastructure, and multi-tenant architectures. Ensures data provenance, lineage, observability, reliability, and correctness while collaborating with ML engineers, data scientists, sport scientists, and domain experts. Requires expertise in Python, SQL, streaming technologies, AWS, graph data modeling, causal inference, AI evaluation, and telemetry systems.

Design, deploy, and operate highly available database infrastructure across multi-cloud, virtualized, and Kubernetes environments. Migrate database services from virtual machines to Kubernetes, support disaster recovery and backup strategies, tune performance, and implement observability, logging, and alerting. Collaborate with engineering teams to resolve infrastructure challenges and automate workflows using scripting and infrastructure orchestration tools.

Designs and operates secure AWS environments, infrastructure as code, CI/CD pipelines, and scalable data lake platforms for Roche’s OneLake initiative. The role uses Python and Boto3 to build automation, APIs, synchronization layers, and serverless solutions, while implementing AWS governance, access controls, and least-privilege security. Responsibilities include Apache Iceberg, AWS Glue, S3, and Snowflake integrations, production troubleshooting, root-cause analysis, stakeholder collaboration, and mentoring.

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.

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.

Design and maintain data processing pipelines and ETL workflows for a banking platform. Integrate APIs, databases, S3 buckets, SFTP servers, and other systems; transform, validate, synchronize, and test data; troubleshoot production integration issues; and collaborate with stakeholders and developers.

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.

Design, build, and manage scalable Databricks data solutions, including batch and real-time ETL/ELT pipelines. Implement Delta Lake, Medallion Architecture, data quality frameworks, CI/CD, governance, lineage, and data mesh practices. Optimize Spark workloads, manage integrations, support data migrations, and collaborate with cross-functional international teams to deliver reliable data for analytics and business intelligence.

Lead the design and hands-on development of scalable Java/Spring Boot backend services, data integrations, APIs, microservices, and distributed event-driven applications. Provide technical guidance, conduct design and code reviews, mentor engineers, and ensure engineering standards for reliability, performance, security, testing, CI/CD, and observability. Collaborate with frontend teams and support AI-enabled data capabilities, including retrieval workflows and intelligent automation.

Lead the architecture and hands-on development of cloud-native data platforms, real-time and batch pipelines, data products, and scalable storage systems. Work with Databricks, Snowflake, AWS, GCP, Spark, Flink, and related technologies. Provide technical direction, establish engineering standards, mentor engineers, ensure data quality and security, and advise clients on pragmatic platform and architecture decisions.