Data Engineer

• Assess current maturity and define a scalable enterprise data platform strategy serving all markets
• Drive a platform-based approach leveraging modern technologies (e.g. Azure, Fabric, Databricks or equivalent)
• Ensure scalable and secure:
• multi-country data ingestion and harmonization
• processing and storage capabilities
• access management and compliance controls
• Establish reusable integration and data engineering patterns across enterprise and operational systems
• Drive the transition from fragmented reporting toward reusable enterprise data products
• Establish scalable data products across core domains:
• Operations
• Finance
• Sales / Commercial
• HR
• Productize operational and management reporting into trusted, scalable, near real-time self-service capabilities
• Enable self-service access to trusted operational data for business users, including operational managers and country functions
• Support business functions in scaling operational transparency and data-driven decision-making
• Operate within a federated data and analytics model where business functions continue to define priorities, KPIs, analytical requirements, and use cases
• Provide shared enterprise capabilities enabling scalable engineering, reusable products, operational enablement, and self-service reporting
• Partner with business stakeholders to translate operational needs into scalable enterprise data products
• Prioritize delivery based on measurable operational and business impact
• Establish scalable data foundations supporting intelligent automation and AI-supported operational workflows
• Enable workflow orchestration and embedded operational decision-support capabilities integrated into enterprise platforms and processes
• Support integration of automation and AI-enabled operational tooling into the enterprise ecosystem
• Ensure alignment between intelligent automation capabilities and enterprise data products, integrations, and operational workflows
• Remain clearly separated from clinical AI development, commercial AI products, and standalone AI research functions
• Define and implement scalable integration patterns across:
• enterprise systems (ERP, CRM, HR)
• operational systems (LIS, RIS, imaging, operational platforms)
• Ensure alignment with enterprise integration platforms and API strategies
• Reduce fragmented and point-to-point data flows through reusable integration and data product patterns
• Drive semantic consistency and interoperability across enterprise data domains
• Establish scalable and compliant enterprise data architectures supporting anonymized and cohort-based data provisioning capabilities for approved analytics, research, operational, approved external research and future data-sharing use cases
• Ensure appropriate anonymization, interoperability, governance, and traceability principles are embedded into enterprise data products and integration patterns
• Establish pragmatic and lightweight enterprise data governance principles focused on scalability, usability, and operational value delivery
• Define and support:
• data ownership alignment
• semantic consistency
• data quality principles
• data contract concepts between source systems and consuming products
• Ensure business accountability for data ownership and usage
• Ensure compliance with GDPR and relevant healthcare regulations
• Implement secure and auditable data access principles
• Define governance and access principles supporting compliant anonymized data usage, cohort-based analytics, and approved external data-sharing scenarios in alignment with regulatory and security requirements
• Define and manage the enterprise data technology stack with strong focus on simplicity, scalability, and cost optimization
• Evaluate and optimize modern cloud-native capabilities and tooling
• Manage relationships with data, integration, and automation vendors and partners
• Demonstrated ability to define and execute enterprise-wide data strategies aligned with measurable operational and business value
• Strong experience in enterprise data engineering, data products, and operational reporting enablement
• Proven track record in delivering scalable self-service and data democratization capabilities
• Ability to operate at executive level, influencing C-level stakeholders and business leaders
• Strong financial and portfolio management capabilities, including investment prioritization, ROI/TCO assessment, and cost optimization
• Drive data literacy and data adoption across the organization
• Experience in regulated environments (healthcare strongly preferred)
• Strong familiarity with modern enterprise data stacks including:
• Azure (Fabric), Databricks or equivalent
• enterprise integration platforms and APIs
• ERP, CRM, LIS/RIS and operational systems integration
• Experience in workflow automation, orchestration, and operational data enablement environments
• Experience working within federated or hybrid operating models and decentralized business environments
• Experience building and scaling high-performing multidisciplinary teams across:
• data engineering
• analytics engineering
• data products
• automation and orchestration capabilities
• Strong vendor and partner management capabilities
• Pragmatic and outcome-driven mindset balancing:
• standardization vs flexibility
• speed vs scalability
• innovation vs cost efficiency
• Establishment of scalable enterprise data foundations adopted across markets
• Delivery of trusted operational reporting and reusable data products across core domains
• Increased adoption of self-service operational reporting
• Reduction of fragmented and duplicated reporting solutions
• Improved operational transparency and data-driven decision-making
• Enablement of intelligent automation and operational workflow orchestration capabilities
• Contribution to operational efficiency and value realization

Head of Data Engineering, Data Products

• Develop and Maintain Pipelines: build and maintain efficient, scalable data pipelines for use cases such as fraud detection
• Support Fraud Analytics: enable analysts and product teams to identify and address emerging fraud patterns through engineered datasets
• Integrate Detection Models: collaborate with teams to operationalise external fraud detection models and integrate them into the data infrastructure
• Data Storage Optimisation: design and optimise data storage solutions for analysing fraud signals and managing historical data
• Feature Engineering: create fraud-specific datasets and features to enhance detection accuracy while supporting business and analytics teams
• Pipeline Monitoring and Optimisation: monitor fraud data pipelines to ensure system reliability and troubleshoot performance issues
• Best Practices Documentation: establish and document best practices for fraud-related data engineering
• Cross-Team Collaboration: partner with data, product, and engineering teams to proactively address fraud trends

Data Engineer, Fraud

• Design, implement, and maintain enterprise data warehouse solutions following Data Vault 2.0 methodology
• Develop and optimise Hubs, Links, Satellites, PIT, and Bridge tables
• Lead conceptual, logical, and physical data modelling activities across multiple initiatives
• Define and promote data architecture standards, modelling guidelines, naming conventions, and documentation practices
• Ensure scalability, performance, reliability, and maintainability of data warehouse architectures
• Provide technical leadership and architectural guidance to Data Engineers and development teams
• Drive the standardisation and automation of ELT processes, metadata management, and deployment practices
• Collaborate closely with Business Analysts, Product Owners, Data Governance teams, and other stakeholders to translate business requirements into robust data solutions
• Support reporting and analytics teams by delivering optimised and business-ready data structures

Senior Data Warehouse Architect, Data Vault 2.0

• Support the design, implementation, and monitoring of ELT/ETL data pipelines.
• Contribute to data platform infrastructure on AWS, including observability and cost optimisation.
• Help maintain and improve CI/CD practices across the data platform.
• Assist with performance tuning and scalability work on Snowflake and pipeline tooling.
• Build Proofs of Concept with new data engineering tools and patterns.
• Explore how generative AI tools (Claude, Agents, MCPs) can extend and automate data platform capabilities.

Data Engineering Intern - Platform

• Building and maintaining data pipelines for collecting, transforming, and integrating data from multiple sources.
• Working with cloud platforms and services such as AWS (S3, Lambda, Glue) and Databricks.
• Developing scalable data processing solutions using Python and related technology stacks.
• Supporting the full data lifecycle, from ideation and ETL/ELT processes to deployment and operationalization of data products.
• Ensuring data availability, quality, and reliability for reporting and analytics initiatives.
• Collaborating with multidisciplinary teams to deliver efficient and scalable data enablement solutions.
• Contributing to reusable, testable, and maintainable codebases following development best practices.
• Supporting version control and collaborative workflows using GitHub.

• Actively design, build, and optimize scalable ETL pipelines for structured and semi-structured data supporting Insights use cases, growth metrics, pubsports, and other out-of-game data sets.
• Design and implement complex data models using industry best practices that capture a complete ecosystem view of in- and out-of-game experiences, while ensuring accuracy, scalability, and long-term usability.
• Architect and implement robust, maintainable, and high-performance data solutions.
• Automate workflows to reduce manual intervention and enhance data processing efficiency, including automation for content, growth, and pubsports areas of coverage.
• Mentor data engineers across all levels, through code reviews and technical guidance, setting best practices and fostering technical growth.
• Optimize query performance and resolve pipeline bottlenecks to improve data accessibility.
• Evaluate and prototype new tools, frameworks, and methodologies to advance data engineering capabilities.
• Support cost optimization by ensuring scalable and efficient data solutions.
• Ensure data quality, governance, and compliance with regulatory standards (e.g., GDPR, CCPA).
• Contribute to the Data Engineering team's discipline by shaping infrastructure, craft standards, tooling, and organizational best practices.

Staff Data Engineer – Product Insights, Gaming Industry

• Drive Innovation by exploring and implementing the latest AWS and big data technologies.
• Collaborate Cross-Functionally with Data Engineers, BI Analysts, and Data Scientists.
• Shape Platform Strategy by contributing to the Data Platform vision and roadmap.
• Ensure Data Integrity by demonstrating exceptional work ethics and integrity.
• Architect Agentic Pipelines for Analytics by designing multi-agent workflows.
• Enable AI-Powered Data Products by driving the adoption of LLM-augmented data capabilities.

Senior Data Engineer – AI, Agentic Pipelines

• Develop ETL/ELT pipelines with AWS Glue.
• Manage governed data lakes using AWS Lake Formation.
• Build event-driven workflows with AWS Lambda and Step Functions.
• Work with DynamoDB for high-performance NoSQL workloads.

AWS Data Engineer

• You develop the end-to-end automation of data pipelines to make ingestion, transformation, and distribution of data more efficient and self-service for our users
• You explore and learn the latest AWS and big data technologies to uncover hidden use-cases, enable new capabilities, and build new integrations for our SIXT Data shop product
• You interact with Data Engineers, BI Analysts and Data Scientists to find the best solutions for different analytical use cases (Dashboarding, ad hoc Analytics, Data-as-a-product, Machine Learning)
• You contribute to the Data Platform vision and execution with experience, new ideas, and your own curiosity
• You show extreme work ethics and integrity to deal with sensitive data while protecting our end-customer

Senior Data Engineer

• Design, develop and maintain data pipelines (batch and streaming) for structured and unstructured data;
• Develop and optimize ETL processes using programming languages such as Python and/or SQL;
• Work with open-source technologies for data processing, integration and storage in on-premises environments;
• Manage and optimize databases, ensuring performance, integrity and availability;
• Ensure data quality, integrity and reliability throughout the data lifecycle (validation, monitoring and testing);
• Design and improve data architectures, ensuring performance, scalability and security;
• Act as a link between Data Scientists, Data Analysts and AI teams, translating needs into technical requirements and data handling;
• Improve the City of Porto's Urban Platform by creating new ways to increase the value and quality of the data collected and used for decision-making that impacts the community's quality of life.

• Design and develop robust data pipelines and ETL/ELT processes using Google Cloud Platform services, including BigQuery, Dataflow, and Pub/Sub
• Build and optimize cloud-based data warehouses and data lakes, ensuring data quality, security, and accessibility
• Write clean, efficient, and well-documented code in Python or Java to transform and process large-scale datasets
• Collaborate with data analysts, scientists, and business stakeholders to understand requirements and translate them into technical solutions
• Implement data modeling best practices and maintain comprehensive data documentation and metadata management
• Monitor and troubleshoot data pipeline performance, identifying bottlenecks and implementing optimization strategies
• Participate in code reviews and contribute to continuous improvement of data engineering practices and standards
• Maintain version control using Git and implement CI/CD practices for data infrastructure deployments
• Support data governance initiatives and ensure compliance with data quality and security standards
• Communicate effectively with distributed teams and provide technical guidance on data engineering solutions

GCP Data Engineer

• Help shape the future of digital (parcel) logistics by developing advanced analytics systems for own and 3rd-party systems.
• Develop analytics applications based on the data provided by systems in a parcel sorting centre.
• Collaborate with Product Management and R&D.
• Work within interdisciplinary development teams, including machine control and system architecture.

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