• Diseñar modelos de datos • Explotar la información mediante las herramientas designadas • Interactuar con los clientes para entender sus requisitos y proporcionar soluciones • Preparar documentación técnica de las soluciones proporcionadas • Asegurar la calidad y la exactitud de los datos en los informes

Role Description Our Data Tribe's mission is to deliver business value in all strategic areas of TDG to become a real data-driven company. In the Data Domain Consumer, we focus on Deutsche Telekom's private customers and create exceptional consumer experiences by harnessing and analyzing data to drive insights-based decision-making. - Provide personalized customer dialogue at every touchpoint. - Ensure omni-channel engagement with the customer at the center of data-driven actions and offers. - Establish and maintain a single source of truth around our customer. - Ensure a unified data flow of TV-relevant data across different systems for holistic analytics. - Focus on customer experience activities, including NPS analytics and dashboards. - Provide data analytics use cases for digital touchpoints of the German NatCo. - Establish common practices for generative AI use cases. As a data engineer, you will be responsible for: - Developing and operating our data pipelines. - Building data layers in OneDataLake (ODL), our Central Data Store Consumer (CDSC). - Balancing business and IT demands to build modern and efficient pipelines in public cloud environments. - Integrating data preparations into managed processes. - Implementing data integrations in Data Vault. - Managing data exports and data operations. Technologies used include Python, PySpark, SQL, and libraries such as Pandas, Matplotlib, Seaborn, Bokeh, Plotly, with pipelines based on Gitlab (Magenta CI/CD). Qualifications - Minimum 3+ years of professional experience as a Data Engineer with proven expertise in big data pipelines and data processing. - Advanced proficiency in SQL, Python, and PySpark for hands-on data transformation and pipeline development. - Demonstrated experience with Git version control and CI/CD practices. - Strong capability to design, build, operate, and troubleshoot data pipelines in production environments. - Proven ability to understand complex data structures and transform them into required structural formats. - Business-fluent English language skills. Requirements - Senior-level experience or equivalent seniority in data engineering. - Hands-on experience with Google Cloud Platform (GCP) technology stack. - Familiarity with Hadoop ecosystem and related big data technologies. - Experience with data modelling, exploration, and architecture within enterprise environments. - German language proficiency. Key Competencies - Practical, hands-on approach to problem-solving (not purely conceptual or advisory). - Ability to quickly assimilate complex data environments and deliver solutions. - Experience integrating data preparations into managed processes and implementing Data Vault integrations. - Familiarity with data visualisation libraries such as Pandas, Matplotlib, Seaborn, Bokeh, and Plotly. Benefits - Please be informed that our remote working possibility is only available within Hungary due to European taxation regulation. Company Description As Hungary’s most attractive employer in 2025 (according to Randstad’s representative survey), Deutsche Telekom IT Solutions is a subsidiary of the Deutsche Telekom Group. The company provides a wide portfolio of IT and telecommunications services with more than 5300 employees. - Hundreds of large customers, corporations in Germany and other European countries. - Received the Best in Educational Cooperation award from HIPA in 2019. - Acknowledged as the Most Ethical Multinational Company in 2019. - Continuously develops its four sites in Budapest, Debrecen, Pécs, and Szeged. - Looking for skilled IT professionals to join its team.

• Spearhead the discovery, evaluation, and integration of new datasets, collaborating (incl. pipeline development and data modeling/documentation) working closely with key data stakeholders to understand their impact and relevance to our core products and the healthcare domain • Facilitate the technical management of data assets - clearly tracking and maintaining context on the data within the dataset lifecycle and sustaining tight partnerships with immediate partners on ingestion & solution data engineering • Translate product / analytical vision into highly functional data pipelines supporting high quality & highly trusted data products (incl. designing data structures, building and scheduling data transformation pipelines, improving transparency etc.) • Set the standard for data engineering practices within the company, guiding the architectural approaches, data pipeline designs, and the integration of cutting-edge technologies to foster a culture of innovation and continuous improvement

• End-to-End Pipeline Engineering: Design, build, and deploy scalable ETL/ELT pipelines from diverse source systems into our Snowflake Data Cloud. • Cloud Infrastructure: Manage and optimize data flows within an AWS environment (S3, Lambda, IAM), ensuring high availability, security, and cost-efficiency. • High-Scale Processing: Leverage Databricks and Python (PySpark) to handle complex data transformations and high-volume workloads. • Implement the Semantic Layer: Collaborate with the team to define, implement, and scale our Semantic Layer (via dbt Semantic Layer, MetricFlow, or similar) to standardize business logic, metrics, and dimensions for all downstream consumers. • Model for Truth: Use dbt to build modular, version-controlled, and tested data models that serve as the definitive foundation for business intelligence.