Data Engineer

• Define and evolve the enterprise data architecture across transactional systems, analytical platforms, semantic models and AI platforms.
• Design the overall information architecture spanning: OLTP systems, Lakehouse, Data Products, Semantic Models, Ontologies, Knowledge Graphs, Context Graphs, Vector Stores, AI Retrieval Architecture.
• Define canonical business entities, relationships, taxonomies and domain boundaries across the enterprise.
• Architect how structured, semi-structured and unstructured information is connected into a unified enterprise knowledge model.
• Design how documents, contracts, communications, workflows and business events are transformed into governed enterprise knowledge.
• Define patterns for entity resolution, relationship management, provenance, confidence scoring and temporal modelling.
• Establish modelling standards for transactional, analytical, semantic and graph workloads.
• Design multi-tenant information models supporting isolation while enabling reusable platform capabilities.
• Work closely with the Data Governance Lead to ensure architecture and governance evolve together through ownership models, metadata, lineage, quality controls and automated guardrails.
• Partner with Product, AI and Engineering to ensure new capabilities build upon shared enterprise information assets rather than isolated datasets.
• Guide engineering teams on data modelling decisions, balancing delivery speed with long-term architectural integrity.
• Ensure new data products align with enterprise information architecture and contribute reusable business capabilities.

Data Architect

• Build and maintain data pipelines that ingest real-time positioning and telemetry data from tracking hardware.
• Design data models and schemas that support real-time event detection (e.g., correlating two tracked objects' positions to infer a pickup or handoff event).
• Implement data quality checks, monitoring, and alerting to catch pipeline failures or data drift early.
• Optimize pipelines for near-real-time processing at the latency the product requires.
• Work with structured and semi-structured data coming from external hardware and API integrations.
• Collaborate with software engineers to ensure data is accessible, reliable, and supports platform features.
• Support deployment, monitoring, and troubleshooting of data pipelines in production.

• The platform adapters that target each design partner's stack (Databricks, Snowflake, BigQuery, AWS), and the abstraction that emerges once a second stack forces it.
• The domain side of the evaluation and verification layer: encoding each platform's idioms and correctness rules so generated output can be trusted.
• The high-value generation targets with partners: ingestion and pipelines, transformation logic, warehouse and catalog scaffolding, orchestration, IaC and CI/CD, and the governance accelerators where partner pain is real (contracts, lineage, metadata, PII, data quality).
• The fit into real delivery: reviewable diffs, integration with partner workflows, the path to adoption.

Senior Data Engineer – Applied AI

• Design, develop, and maintain scalable data pipelines using Databricks, PySpark, Python and SQL.
• Build and optimise batch and near real-time data processing solutions on the Microsoft Azure ecosystem.
• Develop and maintain Delta Lake-based data architectures.
• Develop robust and scalable data products and modern data platform components supporting strategic business initiatives.
• Contribute to CI/CD implementation and automation for data solutions.
• Apply engineering best practices around source control, testing, deployment, and monitoring.
• Support Infrastructure-as-Code and environment standardisation initiatives.
• Ensure high standards of data quality, observability, monitoring, security, and operational reliability.
• Collaborate with Product Managers, Architects, Data Engineers, and business stakeholders to deliver high-quality outcomes.
• Participate in technical design discussions and contribute to solution architecture decisions.
• Support platform governance, cost optimisation, technical documentation, and knowledge sharing initiatives.

Senior Data Engineer, Databricks

• Combine raw data from multiple sources into consistent and machine-readable formats,
• Design, build and maintain data systems and ETL pipelines;
• Monitor, test and optimise data flows;
• Analyse large datasets and uncover meaningful trends and insights;
• Evaluate business needs and translate them into data solutions;
• Explore ways to improve data quality, accessibility and reliability;
• Develop and maintain analytical tools and reporting frameworks;
• Collaborate with analysts, product managers and other stakeholders on cross-functional projects;
• Monitor the cost and efficiency of data infrastructure.

Senior Data Engineer

• Design, build and maintain enterprise data solutions using Microsoft Fabric
• Develop scalable data ingestion pipelines from APIs, databases and file-based sources
• Build and maintain Lakehouse architectures using the Medallion (Bronze, Silver and Gold) approach
• Develop robust data models and semantic layers to support business reporting
• Create and maintain Power BI semantic models and dashboards
• Implement data quality, validation and reconciliation frameworks
• Design secure, governed data solutions including Row-Level Security and access controls
• Build and maintain PySpark notebooks for data transformation
• Develop SQL and Python-based ETL/ELT processes
• Configure and maintain Fabric workspaces, OneLake environments and deployment pipelines
• Work closely with solution architects to implement scalable platform designs
• Produce technical documentation and engineering standards
• Collaborate with UK-based stakeholders and delivery teams
• Support future client projects across the TXP Data & AI Practice

Senior Data Engineer, Microsoft Fabric

• Design and build scalable & reliable data pipelines (ETLs) for our data platform
• Constantly evolve data models & schema design of our Data Warehouse to support self-service needs
• Work cross functionally with various teams, creating solutions that deal with large volumes of data
• Work with the team to set and maintain standards and development practices
• Be a keen advocate of quality and continuous improvement.

• Own backend and data architecture across marketing data pipelines, connector systems, platform APIs, agent/tool runtimes, and downstream product surfaces, defining durable domain boundaries and data contracts between warehouse outputs, platform APIs, agent tools, and user-facing workflows.
• Turn ambiguous platform needs into incremental technical plans that balance reliability, security, tenant isolation, developer experience, and long-term maintainability.
• Lead the evolution of data pipelines following the medallion architecture, driving data quality, schema enforcement, lineage, and curated outputs for analytics and product use cases.
• Harden ingestion from external marketing platforms and related APIs, including connector and reconciliation patterns.
• Architect backend services across API paths and cloud infrastructure, shaping how platform capabilities are exposed through stable contracts, streaming workflows, and tool permissions.
• Implement secure authentication, authorization, rate limiting, and multi-tenant access controls across services.
• Write high-quality, production-ready code and integrate with external APIs, cloud services, and internal microservices.
• Build and maintain deployment pipelines and infrastructure environments, implementing tracing, structured logging, metrics, and alerting to ensure reliability, latency, and operational standards.
• Mentor engineers, contribute to internal libraries, backend frameworks, and drive consistency across the platform.

Principal Data Engineer

• Design, develop, and maintain Dataiku workflows and data pipelines.
• Build data ingestion, transformation, and reconciliation processes.
• Develop SQL and Python solutions supporting business requirements.
• Implement data quality controls and monitoring mechanisms.
• Translate business needs into scalable technical solutions.
• Support data governance and lineage initiatives.
• Contribute to workflow automation and process industrialization.

Data Engineer – Dataiku

• You will be responsible to represent the team in projects and deliver them end to end;
• Act as the single point of contact for the team in large projects;
• Review the work and coach junior team members, including embedded peer review process;
• Design and build data pipelines to collect data from different sources for different purposes and types of consumption across the whole data engineering life cycle;
• Maintaining the existing processes in place, monitoring batch failures, acting accordingly in identifying and resolving the root cause and communicate with stakeholders on resolution and timescales;

• Design, implement, maintain and enhance data integrations in Databricks and Azure Data Factory
• Merge and cleanse data to provide a reliable and curated data layer.
• Engage effectively and proactively with multiple stakeholders to understand requirements.
• Assess and implement suitable data solutions to meet business needs.
• Maintain a comprehensive documentation suite for our data engineering implementations and provide training where necessary to support team members and stakeholders to self-serve their own usage of the data and MI/Reporting.
• Monitor batch and act on failures, identifying root causes.
• Refine business requirements into development tasks, estimating their complexity and dependencies.

• Build and fix pipelines — ingestion, transformation, consumption, all of it
• Write dbt models that actually have tests and docs (yes, we check)
• Chase down a data issue from the report all the way back to the source
• Jump on an incident — MSSQL job died, Snowflake task hung, S3 file vanished — and fix it
• Run a backfill, validate the results, confirm the fix actually worked
• Help move data from MSSQL into Snowflake without losing anything along the way
• Keep an eye on monitoring so users don’t find problems before you do
• Leave things documented well enough that future-you will say thank you

Data Engineer – Middle

• 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.

Data Engineer jobs in Portugal

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Data Architect

Data Engineer

Senior Data Engineer – Applied AI

Senior Data Engineer, Databricks

Senior Data Engineer

Senior Data Engineer, Microsoft Fabric

Data Engineer

Principal Data Engineer

Data Engineer – Dataiku

Senior Data Engineer

Data Engineer

Data Engineer – Middle

Head of Data Engineering, Data Products

Data Engineer, Fraud

Senior Data Warehouse Architect, Data Vault 2.0

Data Engineering Intern - Platform

Data Engineer

Staff Data Engineer – Product Insights, Gaming Industry