Data Engineer

• Define and evolve the group data platform architecture - evaluate and select the target Azure stack (Azure SQL, Synapse, Fabric) and own the migration roadmap to it.
• Design the multi-source integration architecture: ingestion from multiple products with heterogeneous schemas, using an integration-layer pattern suited to many sources (medallion/lakehouse or Data Vault) beneath a dimensional serving layer.
• Address multi-region and data residency requirements: regional hosting, cross-border transfer constraints, GDPR and local privacy law, tenant and country-level data isolation.
• Collaborate with Infrastructure Architects on storage, compute, performance, and Azure cost optimization across regions.
• Design and maintain the three modeling layers: raw/integration (multi-source harmonization), canonical/master data (shared entities such as clinic, patient, device, product across products and countries), and serving (Kimball fact/dimension models optimized for Power BI).
• Define master data management approach: entity matching, survivorship, and golden-record rules across products.
• Optimize analytical models for performance (incremental refresh, partitioning, query tuning).
• Design, build, and maintain ETL/ELT pipelines consolidating data from product services into the platform.
• Build and tune Power BI semantic models and support dashboard development.
• Implement data quality, lineage, and observability - monitoring, alerting, reconciliation checks across sources.
• Collaborate with Domain Architects and product teams to define data contracts and export interfaces from operational services.
• Govern data ingestion - accuracy, performance, and alignment with security and privacy policies.
• Establish and run group data governance: ownership, definitions (a shared business glossary across products), metadata management, documentation, and schema versioning.
• Contribute to ADRs, data architecture diagrams, and data governance documentation.
• Support customer data migration into Manage from legacy systems - mapping, transformation, and validation approaches.
• Define reusable migration tooling and quality gates in collaboration with onboarding/delivery teams.
• Work with business stakeholders across products and countries to translate analytical needs into platform capabilities and data models.

Data Architect

• Build and optimize cloud-native data pipelines on AWS — ETL/ELT and the infrastructure underneath (Aurora, Redshift, dbt, Spark/EMR, Airflow, CDC) — using AI tooling as a standard part of the workflow.
• Design and evolve the data models that power analytics, operational use cases, and AI/ML across thousands of studios.
• Help raise the bar on data reliability — embed governance, testing, and lineage so both people and AI systems can trust the numbers by default.
• Partner with product, and other engineering teams to turn ambiguous business problems into robust data solutions.
• Evaluate and evolve new approaches with us — data mesh patterns, and emerging AI tooling — and help decide what genuinely earns a place in our stack.
• Grow into our financial and regulatory reporting workstream (Finion Capital), where correctness and auditability matter most.

AI-First Data Engineer

• Define company data assets (data model)
• Define and design data integrations, data quality frameworks and design and evaluate open source/vendor tools for data lineage
• Work closely with Dropbox business units and engineering teams to develop strategy for long term Data Platform architecture to be efficient, reliable and scalable
• Conceptualize and own the data architecture for multiple large-scale projects, while evaluating design and operational cost-benefit tradeoffs within systems
• Collaborate with engineers, product managers, and data scientists to understand data needs, representing key data insights in a meaningful way
• Design, build, and launch collections of sophisticated data models and visualizations that support multiple use cases across different products or domains
• Optimize pipelines, dashboards, frameworks, and systems to facilitate easier development of data artifacts

• Working with all DSV business divisions as well as group functions to simplify information gathering and turning data into insight
• Design and implement solutions that will support the development of our Operational Data Warehouse (ODW) in alignment with IT and BI architecture principles, guidelines, and standards
• Develop and maintain scalable data pipelines for ingesting, processing and transforming large volumes of data from various sources
• Analyse requirements together with our different department teams and create effort estimations and implementation planning
• Document and provide technical specifications for the implementations
• Refine and promote our agile delivery framework to help ensure high quality and flexible deliverables on the data & BI platform
• Support different department teams in a collaborative work environment
• Actively transfer knowledge to other team members as well as the Operations & Support team

Senior Data Engineer

• Building data platforms that enable clients and business partners to make efficient decisions.
• Designing robust solutions on state-of-the-art data engines such as GCP, and modern data warehousing solutions BigQuery.
• Solving complex business problems on a large scale.
• Participating in cross-functional initiatives and collaborating across various domains.
• Interacting with engineers, product/project managers, and partners from all over the world.

Senior GCP Data Engineer

• As a GCP Data Architect you will be responsible for designing of solution architecture for organization in a cloud.
• Solving complex business problems at large scale.
• Participating in a cross-functional initiatives and collaborating across various domains.
• Interacting with specialists, product/project managers and partners from all over the world.

GCP Data Architect

• You will design, build and maintain scalable, reliable data pipelines and data platforms supporting analytical and reporting solutions
• You will work on end-to-end data engineering solutions – from data ingestion, through transformation and storage, to serving curated datasets for analytics and reporting
• You will develop and optimize ETL / ELT pipelines using Databricks (Apache Spark, SQL, Python) and Delta Lake technologies
• You will be responsible for data modelling, data structures and performance optimization in analytical data stores (lakehouse / data warehouse)
• You will implement and maintain data quality, data validation and monitoring mechanisms ensuring accuracy, consistency and reliability of processed data
• You will collaborate closely with data analysts, BI developers and business stakeholders to translate business and regulatory requirements into robust technical solutions
• You will contribute to architecture decisions related to data platforms, data processing patterns and technology choices
• You will support and mentor junior data engineers, helping them grow their technical and consulting competencies
• You will actively participate in client-facing work – discussing requirements, presenting solutions and explaining technical concepts in an accessible way
• In addition to project work, you will keep up with latest data engineering, cloud and Anti Financial Crime trends and contribute to internal initiatives and accelerators.

• Design, develop, and maintain highly efficient and scalable ETL processes and data pipelines
• Implement robust data models, ensuring data quality, consistency, and accessibility
• Monitor, troubleshoot, and optimize existing data pipelines and systems
• Architect and implement scalable and resilient data infrastructure on GCP
• Optimize current solutions
• Apply software engineering best practices
• Leverage understanding of agentic AI concepts
• Apply Generative AI techniques and tools
• Help evaluate and integrate AI-assisted development tools
• Work closely with data scientists and business analysts

• Design and evolve the platform’s canonical data model
• Architect and implement scalable data ingestion and synchronization frameworks
• Build reliable, replayable, and highly scalable data processing pipelines
• Design entity resolution and identity management capabilities
• Define and implement data governance, lineage, and provenance standards
• Drive architectural decisions related to scalability, resiliency, security, and operability
• Collaborate with product and engineering teams to define platform capabilities and roadmap
• Design APIs and delivery mechanisms for downstream consumers
• Support customer migrations from legacy systems into the new platform
• Mentor engineers and contribute to technical leadership across the team
• Ensure platform reliability, performance, and operational excellence

Software Developer, Data Engineer

• Design and implement streaming and batch data pipelines for data integration, ingestion, data quality management (quality gating), transformations, and auditing.
• Perform feature engineering and data exploration.
• Collaborate with business and operations teams to align on requirements, and to request and standardize waveform/sensor data.
• Build and maintain the Gold data layer, key KPIs, and PoC evaluation frameworks.
• Set up and maintain solid engineering practices, including CI/CD pipelines, Infrastructure as Code (IaC), version control (Git), unit and integration testing, and proper documentation.

• Design and build scalable, cloud-native data platforms from greenfield to production
• Implement near-real-time ingestion pipelines using event-driven patterns
• Define and enforce platform standards, including Data Lake / Lakehouse principles, medallion architecture, and data contracts
• Refactor and optimise existing Spark and PySpark scripts for performance and maintainability
• Introduce best practices for code quality, testing, and CI/CD across data pipelines
• Drive adoption of AI tooling and agentic workflows within the data engineering team
• Ensure data quality, observability, and reliability across all pipelines and platforms
• Develop self-service tooling and microservices to simplify platform usage for other teams

• Design, implement, and continuously improve systems for data ingestion, processing, storage, and sharing
• Build and optimize data architectures for performance, scalability, and reliability
• Develop and maintain ETL/ELT pipelines using modern tools and frameworks
• Ensure seamless integration and synchronization across systems
• Uphold high standards of data quality, security, availability, and performance
• Collaborate with analysts, software engineers, and business stakeholders to understand data needs and deliver solutions
• Perform code reviews, troubleshoot software, and fix defects
• Implement monitoring and alerting for data workflows
• Gain expertise in a variety of banking processes and products

• Design, build, and maintain scalable data lake solutions and processing pipelines handling large volumes of structured and semi-structured data.
• Build and operate real-time data streaming pipelines using Apache Kafka and its ecosystem (Kafka Streams, Kafka Connect).
• Architect and maintain ETL and ELT pipelines with a focus on data quality, idempotency, and observability.
• Develop distributed data processing applications using Apache Spark (PySpark, Scala), running on Databricks.
• Design and manage both SQL and NoSQL databases used in our data products.
• Build data solutions on cloud infrastructure (GCP, Azure, or AWS), leveraging managed services.
• Apply software engineering best practices to data pipelines: version control, automated testing, peer code review, and CI/CD using tools such as GitLab or Jenkins.
• Own the operational health of the data infrastructure and ETL processes you build.
• Integrate data from internal and external sources via REST and SOAP APIs.
• Actively contribute to InPost's data engineering community — through code reviews, internal documentation, tech talks, and mentoring.

• Design, develop, and maintain data pipelines using Azure Databricks
• Build and optimize data transformations using PySpark and SQL in Databricks
• Implement and maintain Lakehouse architectures using Delta Lake
• Develop ETL/ELT pipelines orchestrated through Azure Data Factory
• Integrate data from multiple sources into the data platform and analytical layers
• Design and maintain data models and data warehouse structures for analytics
• Ensure data quality, scalability, and performance of large-scale data processing pipelines
• Collaborate with BI teams to support Power BI and reporting platforms
• Support and evolve existing SQL Server data platforms and ETL solutions (SSIS) when required
• Contribute to the design of modern cloud-based data architectures

Data Engineer, Azure Databricks

• Design, develop, and maintain data pipelines using Azure Databricks
• Build and optimize data transformations using PySpark and SQL in Databricks
• Implement and maintain Lakehouse architectures using Delta Lake
• Develop ETL/ELT pipelines orchestrated through Azure Data Factory
• Integrate data from multiple sources into the data platform and analytical layers
• Design and maintain data models and data warehouse structures for analytics
• Ensure data quality, scalability, and performance of large-scale data processing pipelines
• Collaborate with BI teams to support Power BI and reporting platforms
• Support and evolve existing SQL Server data platforms and ETL solutions (SSIS) when required

Middle Data Engineer – Azure Databricks

• Leading and developing the Data Engineering team across Poland and Denmark (8 colleagues)
• Owning the engineering backbone of the data platform (ingestion, transformation, orchestration, observability)
• Ensuring stable, predictable, and cost-efficient operation of data pipelines
• Driving engineering standards and best practices (CI/CD, testing, documentation)
• Implementing and enforcing governance on data quality, security, and cost
• Supporting a data mesh model by enabling domains to build and operate their own data products
• Working closely with Data Science and AI initiatives to ensure the platform supports current and future use cases
• Translating strategy into concrete engineering roadmaps and executable delivery plans
• Ensuring compliance with VELUX policies on data, privacy, security, and responsible AI

Head of Data Engineering

• Design and implement scalable data platforms and pipelines across cloud environments (Azure/Fabric, AWS, GCP, Databricks, Snowflake)
• Developing reliable batch, streaming, and near-real-time pipelines
• Ensure adherence to best practices in data governance, lineage, quality, and access control (RBAC/ABAC)
• Establish strong data observability, including monitoring of data freshness and pipeline reliability
• Work closely with data scientists, ML engineers, analysts, and business stakeholders to translate requirements into robust data solutions

• Enable Parexel’s data movement within the Enterprise Data Lake
• Deliver standardized operational data products across clinical, safety, and corporate domains
• Contribute to data lake strategy, engineering, and operations
• Ensure trusted, high-quality, and well-governed data
• Serve as a subject matter expert in data delivery
• Lead engineering activities across data ingestion, transformation, storage, and optimization within the Enterprise Data Lake
• Oversee the performance, quality, reliability, security, and lifecycle management of data in the lake environment
• Ensure scalable engineering pipelines across data domains
• Build, mentor, and lead global data engineering teams
• Promote a culture of innovation, operational rigor, and customer-focused delivery

Data Engineer jobs in Poland

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Data Architect

AI-First Data Engineer

Data Engineer

Senior Data Engineer

Senior GCP Data Engineer

GCP Data Architect

Data Engineer

Senior Data Engineer

Software Developer, Data Engineer

Senior Data Engineer

Senior Data Engineer

Data Engineer

Data Engineer

Data Engineer, Azure Databricks

Middle Data Engineer – Azure Databricks

Head of Data Engineering

Senior Data Engineer

Associate Director, Data Engineering

Data Engineer

Senior Data Engineer