- Design, model, develop and maintain data pipelines to ingest, store, process, and present data.
- Ensure data quality, accuracy, and consistency.
- Collaborate with data architects to ensure data pipelines align with the overall data architecture strategy.
- Perform data transformation tasks, including data cleansing, enrichment, and aggregation, to prepare data for analytics and reporting.
- Integrate data from structured and unstructured sources, ensuring compatibility and alignment with data models and business requirements.
- Automate data transformation processes to improve efficiency.
- Implement and maintain data quality checks and validation processes to identify and resolve data anomalies and errors.
- Monitor data pipelines for data quality issues and implement data quality improvements.
- Collaborate with business stakeholders to define data quality requirements.
- Collaborate with data architects and data scientists to design and implement data models, schemas, and structures.
- Ensure that data models support business reporting and analytics needs while optimizing query performance.
- Maintain data dictionaries and metadata to document data structures and relationships.
- Optimize data storage, retrieval, and query performance by implementing indexing, partitioning, and caching strategies.
- Monitor data processing performance and address bottlenecks as they arise.
- Stay updated with best practices in data processing performance tuning.
- Create and maintain documentation for data pipelines, data transformation processes, and data integration procedures.
- Foster a culture of knowledge sharing within the data engineering team and across the organization.
- Collaborate effectively with cross-functional teams, data stakeholders, and business units to understand data requirements and deliver data solutions that meet business needs.
- Communicate technical concepts and data solutions to non-technical stakeholders in a clear and understandable manner.
- Commercial experience of DataBricks, SQL, Python, Power BI
- Experience in data engineering, including designing and developing data pipelines for retrieval / ingestion / presentation / semantics in an Azure environment.
- Effective communication and collaboration skills to work with cross-functional teams and gather data requirements.
- Skills in data modelling (both structured and unstructured data)
- Power Automate
- Data optimization for performance, scalability, and efficiency.
- Experience in automotive/distribution sector
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Data Engineer - Kraków, Polska - GPC Global Technology Center
Opis
Position Purpose
The Data Engineer is a technical role responsible for designing, developing, and maintaining data pipelines. The pipelines will be realized in alignment with the future data architecture and the engineer will collaborate in cross-functional teams to gather requirements and develop the conceptual data models. This role plays a crucial part in driving data-driven decision-making across the organization, ensuring data availability, quality, and accessibility for various business needs.
Responsibilities
Data Pipeline Development
Data Transformation and Integration
Data Quality Assurance
Data Modelling and Schema Design
Performance Optimization
Documentation and Knowledge Sharing
Collaboration and Stakeholder Engagement
Requirements
Essential:
Desirable:
Benefits nad Perks
Location
Krakow/Hybrid