• Apoiar a construção e manutenção (processo e modelagem completa) do nosso data lake nas camadas silver e gold. • Apoiar a construir e manter pipelines de dados robustos e escaláveis. • Automatizar processos de coleta, transformação e integrações de dados. • Garantir a qualidade, consistência e governança completa dos dados. • Trabalhar em parceria com analista, cientista de dados, engenheiro e stakeholders de produto. • Realizar refinamento, documentação e catalogação de projetos e métricas. • Apoiar ativamente as áreas de negócio com dados confiáveis para tomada de decisão. • Atuar e construir ativamente a partir da camada gold de dados dashboards estratégicos.

• Design, implement, and operationalize data governance frameworks across client engagements, covering the following areas: • Define and implement data governance frameworks, policies, and standards tailored to client environments. • Establish data ownership, stewardship models, and accountability structures across business and technical teams. • Develop and maintain data dictionaries, business glossaries, and classification taxonomies. • Ensure compliance with data regulations and internal data management policies (GDPR, BCBS 239, etc.). • Design and implement data quality rules, profiling routines, and monitoring dashboards. • Investigate and remediate data quality issues across structured and unstructured data sources. • Map and document end-to-end data lineage to support auditability and impact analysis. • Define and track data quality KPIs and SLAs in collaboration with data owners. • Deploy and administer data catalog and metadata management tools (e.g. Informatica, Collibra, Alation, Microsoft Purview). • Enrich metadata assets with business context, ownership, sensitivity classification, and usage information. • Drive adoption of the data catalog across business and technical teams. • Configure and operate data governance platforms, primarily Informatica (IDMC, Axon, EDC, DQ) and equivalent tools. • Integrate governance tooling with existing data pipelines, warehouses, and BI environments. • Automate data quality checks and governance workflows within ETL/ELT pipelines. • Provide internal training and knowledge-sharing sessions on data governance best practices. • Support the Team Lead/Manager on client relationships, business development, and internal projects.

• Build and scale web scraper infrastructure • Use LLMs to structure unstructured payer policy text • Stand up CI/CD and reliability improvements across the data pipeline • Ensure that the data infrastructure runs reliably and accurately • Scale infrastructure without constant direction

• Own the design, implementation, and evolution of certified data models, semantic layers, and trusted business metrics that become the foundation for reporting and decision-making across TechnologyAdvice. • Partner directly with Finance, Revenue, Marketing, Editorial, Product, and Engineering stakeholders to understand business questions, define trusted metric definitions, and translate ambiguity into scalable data solutions. • Design, build, and maintain analytics models, transformations, and data products using modern analytics engineering practices. • Establish and continuously improve data quality through automated testing, validation, monitoring, and observability to ensure business stakeholders can trust the data they use every day. • Improve the architecture, organization, and maintainability of our analytics platform, continuously identifying opportunities to simplify, standardize, and modernize our Business Intelligence ecosystem. • Collaborate with DevOps and Software Engineers to improve data ingestion, lineage, orchestration, governance, and platform reliability across the analytics stack. • Apply AI-assisted development practices to accelerate SQL development, documentation, testing, root cause analysis, and engineering productivity while maintaining rigorous standards for data quality and accuracy. • Document business definitions, data models, metric logic, and architectural decisions to improve transparency, onboarding, and long-term maintainability. • Continuously evaluate new technologies, AI capabilities, and analytics engineering practices that improve data quality, governance, developer productivity, and stakeholder confidence. • Own trusted data products end-to-end—from business discovery and metric definition through implementation, validation, adoption, and continuous improvement. • Champion a culture of trusted data by helping teams adopt certified data models and reducing duplicate business logic across reports, dashboards, and applications.