Opinov8

• 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.

• Actively design, build, and optimize scalable ETL pipelines for structured and semi-structured data supporting Insights and Finance use cases that analyze technology usage and costs (e.g. AWS CUR). • Design and implement complex data models using industry best practices that capture a complete view of technology usage and costs, while ensuring accuracy, scalability, and long-term usability. • Partner closely with internal stakeholders to understand their needs and craft solutions that drive their ultimate outcomes. • Architect and implement robust, maintainable, and high-performance data solutions. • Automate workflows to reduce manual intervention and enhance data processing efficiency, including automation for finance processes. • Mentor junior data engineers through code reviews and technical guidance, setting best practices and fostering technical growth. • Optimize query performance and resolve pipeline bottlenecks to improve data accessibility. • Ensure data quality and governance.

• Design, build, and maintain scalable ETL/ELT pipelines using Azure Databricks and Apache Spark; • Develop and optimize PySpark/Spark SQL notebooks for large-scale data processing; • Implement batch and real-time data ingestion pipelines using Azure Data Factory (ADF), Databricks Workflows, and Structured Streaming; • Manage and optimize Delta Lake/Lakehouse architecture, including Bronze, Silver, and Gold layers; • Integrate data from multiple sources including APIs, databases, cloud storage, and third-party systems; • Work with Azure services such as ADLS Gen2, Azure Synapse Analytics, Azure SQL Database, and Azure DevOps; • Monitor, troubleshoot, and improve pipeline performance, scalability, and cost optimization; • Implement data governance, security, access controls, and compliance standards using Unity Catalog and RBAC; • Collaborate with Data Engineers, Data Scientists, Analysts, and Product teams to deliver reliable data solutions; • Support CI/CD implementation, automation, and infrastructure-as-code practices.

• Own the full delivery cycle: sprint planning, execution, release, and retrospectives • Proactively identify and remove blockers, escalating with context and solutions • Use Scrum/Kanban metrics (velocity, cycle time, throughput) to improve delivery flow • Manage cross-team dependencies without slowing down progress • Actively use AI tools for reporting, sprint summaries, and process automation • Introduce and scale AI-driven practices in delivery workflows • Control scope and manage mid-sprint changes with clear communication • Translate stakeholder needs into actionable tasks and acceptance criteria • Foster a high-trust, psychologically safe team environment • Drive continuous improvement through actionable retrospectives

• Own and maintain the migration backlog - keep it prioritized, refined, and ready so the team always knows what's next and why; • Run refinement sessions that actually refine - break down work, clarify scope, resolve ambiguity before it reaches development; • Be the main point of contact for customers during migration - understand their data, their systems, and their concerns, and translate that into clear requirements for the team; • Ask the right technical questions - you don't need to write the code, but you need to understand how APIs work, what a data mapping looks like, and where integrations tend to break; • Work with engineering to scope migration paths - what data moves, how it moves, what needs transformation, and what gets left behind; • Spot risks early - data quality issues, missing fields, incompatible formats - and make sure the team addresses them before they become blockers; • Coordinate across teams when migrations touch other parts of the platform; • Track migration progress and communicate clearly to customers and internal stakeholders.

• Design and implement model validation frameworks: accuracy, precision, recall, F1 across clinical subgroups (age, hearing loss severity, device type) • Write regression tests for model updates — detect silent accuracy degradation before any deployment to production • Design adversarial test cases: edge cases, out-of-distribution inputs, boundary conditions, and clinically implausible inputs • Design and maintain API test suites for AI feature endpoints consumed by the PMS frontend and microservices • Write contract tests between AI services and consuming services — prevent integration breakage when models are updated or retrained • Test latency, throughput, and graceful degradation under load • Validate error handling: model confidence thresholds, fallback behaviour when models are unavailable or return low-confidence outputs • Collaborate with the AQA on shared Playwright E2E coverage for AI-integrated UI flows • Design testing evidence for ISO 42001 AIM - Traceability from AI system requirements to test cases to results, supporting audit and certification • Design and implement model monitoring pipelines - track accuracy, confidence distribution, and prediction drift in production against baseline • Contribute to post-incident reviews when AI features cause unexpected clinical workflow impacts or regulatory flags • Feed production monitoring findings back into the regression test suite to prevent recurrence and improve model robustness

• Coordinate and drive execution of marketing initiatives across multiple regions • Act as the main communication bridge between teams in Germany and Brazil • Ensure alignment on priorities, timelines, and deliverables • Organize and facilitate regular sync meetings and updates • Track campaign progress and proactively manage risks or blockers • Support planning and execution of digital marketing campaigns • Collaborate with internal stakeholders to ensure smooth delivery

• Design, develop, and execute test plans, test cases, and test scripts for web and backend applications; • Perform API testing using tools like Postman and validate endpoints based on Swagger/OpenAPI documentation; • Develop and maintain automated test scripts using JavaScript or Python; • Validate REST APIs, ensuring performance, reliability, and data integrity; • Collaborate with developers, product managers, and DevOps teams to ensure quality throughout the SDLC; • Integrate automated tests into CI/CD pipelines to support continuous testing; • Perform database testing using SQL queries to validate data accuracy; • Identify, document, and track bugs, and support their resolution; • Participate in code reviews, test strategy discussions, and continuous improvement initiatives; • Mentor junior testers and contribute to best practices in QA.

• Define and drive the AI technical roadmap, architecting scalable solutions using LLMs, RAG, and Vector Databases. • Lead the design and implementation of LLM Ops pipelines — covering model deployment, monitoring, versioning, and evaluation. • Evaluate and integrate emerging AI tools and frameworks, including Model Context Protocol (MCP) and agentic architectures. • Lead, mentor, and grow a multidisciplinary team of AI and ML engineers, fostering a high-performance culture. • Oversee end-to-end delivery of AI features — from prototyping to production — using Docker, Git, and SQL-based workflows. • Collaborate cross-functionally with product, engineering, and data teams to align priorities and translate AI capabilities into business value.

• Design, develop, and maintain robust Python-based backend services and APIs (REST and GraphQL) within a microservices architecture. • Work with both SQL and NoSQL databases to model, query, and optimize data storage for performance and scalability. • Integrate services with Azure cloud infrastructure, leveraging managed services for compute, storage, and messaging. • Write clean, testable code and participate actively in code reviews, contributing to team standards and best practices. • Collaborate with product, data, and frontend teams to deliver end-to-end features that solve real maritime challenges. • Troubleshoot and resolve issues in production, supporting the reliability and observability of backend systems.