Role Description As our CPO, you'll own the entire product lifecycle—from vision to execution. You'll define the product strategy in alignment with our technical roadmap, market needs, and user feedback. You’ll work closely with engineering, marketing, and executive leadership to shape innovative Web3 products that solve real problems and scale globally. Responsibilities - Product Strategy & Vision: - Define and execute a compelling product vision aligned with our company’s mission. - Stay ahead of trends in crypto, DeFi, NFTs, and Web3 to guide long-term product planning. - Team Leadership: - Build, mentor, and lead a high-performing team of Product Managers, Designers, and Analysts. - Foster a culture of innovation, user-centricity, and continuous improvement. - Execution & Delivery: - Prioritize product features using data, user feedback, and business goals. - Collaborate with engineering to ensure timely, high-quality releases. - Define and track KPIs and success metrics for product performance. - Stakeholder Collaboration: - Partner with marketing, community, compliance, and operations to ensure product-market fit and regulatory readiness. - Communicate product plans and progress to the executive team, investors, and community. Qualifications - Proven experience (8+ years) in product leadership, ideally with 5+ years in a crypto/blockchain company. - Deep understanding of crypto protocols, DeFi, wallets, exchanges, tokens, and/or smart contracts. - Strong background in user-centered design, agile development, and lean product methodologies. - Experience scaling consumer or developer-facing products in fast-paced environments. - Excellent communication skills with both technical and non-technical stakeholders. - Passion for Web3 and a strong belief in decentralized technology. - Level of studies: Bachelor’s or Master’s degree in Computer Science or related field. - Language Requirements: Fluent in English. Benefits - Competitive Financial Compensation: We offer financial benefits that reflect the value of your work and dedication. - Work Flexibility: Enjoy the flexibility to work from home, the office, or even abroad. - Annual Holidays: Generous paid time off to help you maintain a healthy work-life balance. - Relocation Assistance: We provide relocation support for employees moving to new locations. - Professional Development: Opportunities for training, certifications, and career growth. - Employee Recognition: Programs to celebrate and reward your achievements and contributions. - Inclusive & Collaborative Culture: A supportive and diverse work environment where your voice matters. - Diversity, Equality, and Inclusion: We are committed to fostering a workplace that values diversity and promotes equality and inclusion for all employees.

Role Description We are seeking a highly skilled and experienced Senior Data Engineer with solid expertise in AWS, Azure & GCP, along with proficiency in modern data transformation tools such as dbt and Databricks. - Conceptualizing and implementing scalable and secure cloud-based data solutions. - Shaping our cloud data strategy to ensure our data infrastructure aligns with organizational requirements and advances business objectives. - Collaborating closely with esteemed global clients to deliver top-tier solutions in the expansive field of Data & Analytics. Qualifications - 5+ years of professional experience in the Data & Analytics field. - Proven experience as a Cloud Data professional, with expertise in AWS, Azure & GCP, dbt & Databricks. - Solid knowledge of cloud data services, data warehousing, big data technologies, and data lakes. - Proficiency in data modeling, data integration, and ETL processes. - Good grasp of business processes and the ability to promote automation through effective management. - Proficient knowledge and understanding of architecture, application design, systems engineering, and integration. - A bachelor’s degree in computer science, information technology, or a related field. A master’s degree is a plus. Requirements - 5+ years of professional experience in the Data & Analytics field. - Proven experience as a Cloud Data professional, with expertise in AWS, Azure & GCP, dbt & Databricks. - Solid knowledge of cloud data services, data warehousing, big data technologies, and data lakes. - Proficiency in data modeling, data integration, and ETL processes. - Good grasp of business processes and the ability to promote automation through effective management. - Proficient knowledge and understanding of architecture, application design, systems engineering, and integration. - A bachelor’s degree in computer science, information technology, or a related field. A master’s degree is a plus. Benefits - Challenging and innovative big data projects. - Continuous learning and career development in a fast-growing data-driven industry. - Competitive salary package. - Flexible and supportive work environment.

• Design, develop, and maintain robust ETL/ELT pipelines to ingest, transform, and deliver data across enterprise platforms. • Build scalable data ingestion frameworks for structured and semi-structured data, including XBRL filings and financial datasets. • Implement data transformation logic to support analytics, reporting, and regulatory use cases. • Ensure data pipelines are reliable, performant, and scalable in cloud environments. • Leverage AI-assisted development tools to accelerate pipeline development, testing, and optimization. • Develop and manage data solutions leveraging AWS services (e.g., S3, Airflow, DAGs, Glue, Lambda, Redshift). • Implement and optimize Apache Iceberg table formats for large-scale, ACID-compliant data lakes. • Support lakehouse architectures that unify data lakes and data warehouses. • Optimize data storage and retrieval strategies for performance and cost efficiency. • Enable data platforms that support AI/ML workloads and downstream generative AI use cases. • Design and implement CI/CD pipelines for data pipelines, infrastructure, and analytics code using various tools. • Automate build, test, and deployment processes for ETL pipelines and data platform components. • Implement DataOps best practices, including version control, automated testing, environment promotion, and rollback strategies. • Ensure reproducibility, reliability, and governance of data pipeline deployments across environments. • Integrate AI-driven testing and monitoring tools to improve pipeline quality and reduce operational risk. • Design and implement materialized views and other performance optimization techniques to improve query efficiency. • Develop pipelines to ingest, parse, and normalize XBRL data. • Apply context engineering principles to ensure data is enriched with meaningful metadata, lineage, and business context. • Collaborate with data architects, analysts, and business stakeholders to understand data needs and deliver solutions. • Work in Agile teams to iteratively deliver data capabilities and enhancements.

• Design and implement scalable enterprise data architectures leveraging AWS and Apache ecosystem technologies (e.g., Spark, Iceberg). • Architect modern AI-enabled data platforms, including support for machine learning, LLM integration, and retrieval-augmented generation (RAG) patterns. • Develop and maintain conceptual, logical, and physical data models, including Entity Relationship Diagrams (ERDs). • Architect modern data lakehouse and data warehouse solutions using Apache Iceberg and cloud-native services. • Define and enforce standards for data integration, data quality, and data lifecycle management. • Design and implement Knowledge Graph architectures, integrating structured and unstructured data sources. • Design and implement Knowledge Graphs and semantic data layers using ontologies, taxonomies, and linked data principles. • Apply GraphRAG architectures to enhance LLM-based applications with context-aware, explainable data retrieval. • Develop and manage ontologies and semantic models to enable interoperability, data discovery, and advanced analytics. • Integrate AI/ML and generative AI capabilities into enterprise data ecosystems, including vector databases and embedding pipelines. • Ensure alignment between data architecture and AI governance, including model transparency, traceability, and responsible AI practices. • Establish and manage enterprise metadata frameworks, including data dictionaries, business glossaries, and technical metadata repositories. • Lead or support stakeholder listening campaigns to gather input from executives, data leaders, and practitioners across the enterprise. • Collaborate with stakeholders to identify data challenges, AI use cases, and opportunities for advanced analytics and automation. • Support analysis of alternatives (AoA) for data and AI tools/platforms, providing recommendations based on cost, capability, and mission fit. • Track and report on data strategy progress, maturity improvements, and program outcomes.