Top Data Engineer Jobs in Krakow

Design, build and maintain Snowflake-based data warehouses and dbt models; write advanced SQL; automate pipelines with CI/CD and container concepts; use Git workflows; perform data processing with Python. Provide German-language support (minimum B2).

The Data Engineer will design and implement scalable data platforms, build data pipelines, collaborate with teams to ensure data quality, and streamline processes using AWS services and big data technologies.

The Data Engineer will develop, maintain, and optimize data pipelines using Apache Airflow, manage database performance, and support Elasticsearch integration. Responsibilities include building ETL scripts, handling Unix/Linux operations, and implementing CI/CD pipelines in GitLab.

Design, build, and maintain ELT pipelines and dbt SQL models for analytics; optimize performance, scalability, and costs; apply AI-enabled tools to improve engineering workflows; collaborate to translate requirements into production-grade data assets.

Design and implement backend systems, ETL pipelines, data storage, and APIs for a SaaS platform. Maintain platform availability and improve security, quality, testing, automation, documentation, and features. Support customers and staff, contribute to sprint planning and prioritization, estimate development effort, track project progress, and proactively resolve roadblocks.

Develop feature stores and other data services, build ELT and ETL pipelines, integrate data-processing and storage tools, and support scalable, high-load systems. The role requires expertise in Python, SQL, event-driven systems, streaming pipelines, system design, APIs, message brokers, and cloud data technologies. It is fully remote and involves operational data-engineering tasks.

Own end-to-end AI-automated data platform migration projects (1-4 concurrently): scope, plan, execute, and hand off. Act as primary customer contact, configure Datafold's Migration Agent, partner with engineering on execution, and help refine delivery playbooks.

Owner of marketing-focused data engineering: build and maintain integrations, tracking, and pipelines across BigQuery, ClickHouse, GCS, and external systems; troubleshoot tracking, payment, and data quality; collaborate with cross-functional teams and document data flows.

Leads the design and development of enterprise data warehouses, lakes, and governed data platforms. Builds scalable batch and real-time ETL/ELT pipelines using Databricks, SQL, Python, and PySpark; drives legacy migration and enterprise data integration. Enables analytics, automation, and AI use cases while establishing engineering standards, mentoring technical teams, and partnering with stakeholders on strategic data transformation initiatives.

Design, build, and maintain scalable batch and streaming data pipelines, internal tools, and APIs to support analytics, product features, and ML. Collaborate with product and engineering to improve data infrastructure, ensure reliability, testing, and observability in a cloud-native environment.

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.

Build and ship full-stack features for a data intelligence SaaS platform, spanning backend services and frontend interfaces. Use AI coding assistants to accelerate development while applying sound engineering judgment. Participate in architecture discussions, code reviews, testing, incident response, troubleshooting, documentation, and knowledge sharing. Partner with product managers to translate requirements into technical solutions. The role emphasizes SaaS architecture, AWS cloud fundamentals, Docker, Kubernetes, CI/CD, agile delivery, autonomy, and production ownership.

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.

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.

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, 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.

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

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.

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 Senior Data Engineer will develop databases, data lakes, and data ingestion pipelines, ensuring scalable solutions and engaging with stakeholders across various departments.