Top Data Engineer Jobs in Warsaw

Design, build, and maintain scalable batch and real-time data pipelines, ELT workflows, cloud data infrastructure, orchestration, and streaming solutions. Ensure production reliability, performance, security, governance, monitoring, and data quality while collaborating with technical and business stakeholders. Participate in on-call support and continuous improvement initiatives.

Lead design and delivery of end-to-end ML/AI solutions: architect systems, implement and validate models, oversee data pipelines, coordinate cross-functional teams, mentor engineers, liaise with clients, and ensure timely Agile delivery.

Join the Customer Data Platform team to deliver production services via Claude Code, own event pipelines, Elasticsearch query/search, sandboxed customer logic, Kafka-backed systems, PostgreSQL-backed services, Kubernetes/Helm/GitOps deployments, coordinate cross-team integrations, scope epics, run on-call rotations, and drive platform custodianship including dependency and CVE migrations.

Build scalable pipelines to ingest and process high-volume unstructured insurance documents, including PDFs, scans, emails, Word, Excel, and PowerPoint files. Responsibilities include OCR integration, document parsing, text normalization, semantic chunking, metadata extraction, enterprise connectors, retrieval-ready data preparation for RAG systems, validation, monitoring, logging, testing, and secure cloud deployment using software engineering best practices.

Develop backend and data solutions while supporting daily operations, collaborating with cross-functional stakeholders, improving workflows, monitoring performance metrics, tracking milestones, solving complex challenges, maintaining documentation, and ensuring compliance. The role requires strong communication, prioritization, independent troubleshooting, and the ability to deliver high-quality outcomes in a fast-paced environment.

Design and optimize Snowflake data models, customer 360 views, identity resolution, segmentation, and CDP-ready schemas. Build pipelines syncing customer data to Braze, Hightouch, and related platforms. Improve performance, cost efficiency, data quality, governance, consent management, and audience activation while collaborating with CRM, marketing, analytics, and technical stakeholders.

Lead data and analytics projects: design dimensional models, build Power BI dashboards, run exploratory analysis, capture client requirements, develop end-to-end data pipelines on Azure, test and assure solutions, support client decision-making, and contribute to business generation and people development.

Lead delivery of end-to-end data and analytics solutions: build dimensional models, develop Power BI dashboards, perform exploratory analysis, design Azure-based data pipelines, capture client requirements, run scenario modelling, test and assure solutions, prepare client communications and lead project teams.

Build, deploy, and maintain large-scale analytics and data-processing solutions. Design data ingestion, streaming, batch processing, transformation, and storage components across distributed platforms. Own infrastructure components, improve reliability and performance, collaborate with engineering, analysts, and data scientists, and support QA, DevOps, documentation, mentoring, customer meetings, and sales activities.

Build and maintain Camunda 8 backend data-layer services across relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, streaming, and secondary-storage solutions; investigate production data issues; improve performance and operability; and deliver engineering initiatives collaboratively with Product, QA, Support, and SRE. Participate in L3 support and on-call rotations.

Design, build, test and support batch and streaming data pipelines and curated datasets on a modern lakehouse/AI platform. Improve data modelling, quality, reconciliation, performance and operational reliability, partner with stakeholders, and provide technical leadership for delivery.

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 scalable data pipelines for BigQuery migrations. Develop ingestion and transformation frameworks, orchestrate and optimize workloads, ensure data quality, and deliver production-grade data products for reporting and analytics. Collaborate with analytics engineers, architects, and stakeholders on platform migration and sustainability. Required capabilities include Python, SQL, BigQuery, ETL/ELT, data modeling, orchestration, Git, CI/CD, cloud-native architectures, testing, monitoring, and data validation.

Lead mechanical due diligence and feasibility for data center sites and deals. Validate cooling topology and equipment strategy (air, liquid, hybrid), assess capacity, vendor lead times, and integration with budgets and schedules. Define mechanical readiness gates, deliver validated scope to Delivery, coordinate with stakeholders and vendors, and help build repeatable evaluation standards for AI/hyperscale infrastructure acquisitions.

Own and operate Databricks production data pipelines for a predictive CRM platform: monitoring, incident response, cost and performance optimization, migrating legacy ETL, onboarding customers, enforcing Delta Lake architecture and multi-tenant isolation, and applying CI/CD, security, and observability best practices to ensure reliable, scalable data delivery.

Design, build, and own scalable data products on Snowflake/AWS using dbt and Data Vault. Implement ETL/ELT pipelines, CI/CD releases, data quality/observability (Monte Carlo), and governance (Collibra, Immuta). Partner with product and architecture teams, support release automation, and establish engineering best practices within a Data Mesh framework.

Design and implement data models, build ETL workflows and Data Marts, and develop automated reporting dashboards for credit risk. Collaborate with data architects, prepare documentation, and translate business requirements into scalable reporting solutions.

The role involves implementing AI features, improving retrieval quality, collaborating with teams, and building backend APIs for various projects.

Build and operate large-scale web data extraction and scraping systems, design ingestion and processing pipelines, ensure data quality and governance, optimize performance and reliability, implement observability, and collaborate with product and engineering teams to deliver production-grade APIs, backend services, and cloud-deployed workflows.

Lead design and delivery of Databricks-based Lakehouse solutions and end-to-end data engineering projects. Build and optimize ETL, streaming and batch pipelines, integrate cloud and on-prem systems, mentor team members, support pre-sales, and ensure governance, security, and platform performance.

Design and implement enterprise knowledge graph solutions, build semantic models/ontologies, optimize graph data models and queries, integrate graphs with Data & AI platforms (RAG/Agentic AI), and collaborate with cross-functional teams and clients on graph and AI initiatives.

Design, implement, and maintain backend systems, data pipelines, storage, and APIs for a SaaS platform. Ensure platform availability, data quality, security, testing, automation, and documentation. Support customers and internal staff, contribute to sprint planning and estimations, and track project progress.

Own the finance data domain by developing financial data products and models, integrating and improving data quality, monitoring freshness and completeness, and resolving reconciliation issues. Build reports, alerts, automations, and LLM agents using Python, SQL, Snowflake, Airflow, and dbt. Communicate data issues clearly to Finance leadership and ensure financial reporting and planning data remains accurate and reliable.

The Staff Engineer will define architecture, lead data pipeline design, ensure platform reliability, and mentor engineers, driving data initiatives and technical standards.

Design and maintain data warehouse systems, develop ETL pipelines, manage datasets for analytics, and implement real-time data solutions.