• Lead current state assessment of metadata systems and identify gaps • Design and build common metamodel and enterprise data model • Develop and enhance metadata utility platform • Integrate multiple data formats and systems into unified model • Create data dictionary and standardized data definitions • Drive future state architecture and enhancements • Support testing, validation, and production deployment • Manage CI/CD setup and system environments • Conduct knowledge transfer and documentation • Coordinate with program manager and stakeholders on priorities and delivery

Role Description - Desenvolver e otimizar pipelines de dados em larga escala com foco em desempenho e confiabilidade; - Atuar na construção de soluções de streaming e batch para ingestão, transformação e consumo de dados; - Colaborar com arquitetos e desenvolvedores na definição de padrões técnicos e boas práticas; - Garantir observabilidade, versionamento e qualidade dos dados em produção; - Apoiar iniciativas de modernização e automação de processos dentro da tribo; Qualifications - Experiência sólida como Engenheiro(a) de Dados Sênior; - Proficiência em Scala, Spark (Streaming) e Python; - Conhecimento prático em arquiteturas distribuídas e microsserviços; - Vivência em processamento de dados em nuvem (AWS); - Capacidade analítica e pensamento voltado à eficiência e escalabilidade; Requirements - Experiência com ScyllaDB (ou Apache Cassandra); - Conhecimento em Kafka; - Vivência com serviços AWS como Lambda, SNS, SQS e Glue; - Experiência em ambientes de dados voltados a produtos digitais e times;

• Own the strategy, vision, and roadmap for the PDL as a governed data product — engaging external and internal consumers, the Analytics Chapter, and engineering leadership to define, validate, and continuously iterate the direction. • Own the commercial narrative for the PDL as an external product offer — defining what ENSEK’s data capability means to an energy retailer’s analytical team, how it is differentiated, and working with commercial partners to validate positioning and identify the investment required to productise it at scale. • Make evidence-based prioritisation decisions, estimating the effort and value of roadmap items, using AI-powered estimation tools to improve accuracy, and discussing trade-offs transparently with stakeholders. • Define and track key product outcomes — implementing dashboards for real-time performance visibility, and using data to drive continuous improvement in quality and user experience. • Represent users confidently in internal discussions, advocating for research across all user types and leveraging AI-powered tools to accelerate insight generation at scale. • Advocate for product-led thinking within your team, consult with stakeholders to protect the team from unnecessary directional changes, and champion AI tool adoption. • Build long-term stakeholder relationships, implementing communications strategies and influencing effectively to drive alignment and outcomes.

• Design and build data pipelines for large volumes of payment data that are performant, reliable, and correct — not just fast. • Own end-to-end data flows: from ingestion and transformation through to the outputs that Finance, Product, and clients depend on. • Drive data quality across your domain with tooling. • Work cross-functionally with Product, Finance and enable other Engineering teams via a 'consulting' style model. • Contribute to how the team works — code review culture, CI/CD standards, ADRs, how we handle incidents — we're building these practices now and senior engineers shape them. • Help onboard and level up engineers around you; there's real opportunity to make an impact here.