About Eneba At Eneba, we’re building an open, safe and sustainable marketplace for the gamers of today and tomorrow. Our marketplace supports close to 20m+ active users (and growing fast!), provides a level of trust, safety and market accessibility unparalleled to none. We’re proud of what we’ve accomplished in such a short time and look forward to sharing this journey with you. Join us as we continue to scale, diversify our portfolio, and grow with the evolving community of gamers. About the team We are a mobile application where users earn rewards by playing games. Our Data Engine team is at the heart of this ecosystem, working in a high-impact environment to build the recommendation systems and ML models that drive our product's growth. You will join a specialized squad of Data and ML Engineers focused on optimizing user journeys and scaling our platform toward full profitability. We are a lean, high-ownership team where every engineer manages their pipelines from A to Z. What it’s like to work at Eneba *Opportunity to join our Employee Stock Options program. *Opportunity to help scale a unique product. *Various bonus systems: performance-based, referral, additional paid leave, personal learning budget. *Paid volunteering opportunities. *Work location of your choice: office, remote, opportunity to work and travel. *Personal and professional growth at an exponential rate supported by well-defined feedback and promotion processes. *Please attach CV's in English. *To find out about how we handle your personal data, make sure to check out our Candidate Privacy Notice https://www.eneba.com/candidate-privacy-notice

• Design and maintain Snowflake data models that support customer engagement, reporting, and audience activation • Build and optimise customer-level data structures for segmentation, personalisation, and lifecycle marketing • Translate customer engagement requirements into scalable Snowflake models and CDP-ready schemas • Create and maintain pipelines that sync customer data into Braze, Hightouch, and other downstream tools • Work with identity resolution, consent frameworks, event tracking, and customer profile unification • Support clients in building customer 360 views and improving audience readiness across channels • Optimise Snowflake performance, cost efficiency, and scalability across large datasets • Ensure data quality, governance, and compliance across customer-facing data models • Collaborate closely with CRM, CDP, analytics, and marketing teams to ensure data supports business use cases

• Design, build, and maintain scalable ETL/ELT pipelines across internal and external systems • Develop and manage integrations between data warehouses, APIs, CDPs, CRM tools, and marketing platforms • Build and optimise data ingestion frameworks across batch and real-time architectures • Manage API integrations, webhook frameworks, file-based ingestion, and event-driven pipelines • Work closely with engineering and platform teams to ensure data is structured, accessible, and reliable • Improve observability, monitoring, alerting, and performance across data pipelines and platform integrations • Ensure robust governance, security, and documentation standards across all integrations • Troubleshoot data delivery issues, system failures, and performance bottlenecks • Support the implementation of scalable architecture patterns that can be reused across multiple clients

• Collaborate with Data Architects and business partners to design and evolve enterprise data architecture and platform capabilities. • Translate architectural strategy into technical designs and delivery plans across teams. • Design, code, and optimize complex distributed data processing systems using Spark, Databricks, and cloud‑native data services. • Develop canonical data models, semantic structures, and reusable datasets to support reporting and machine learning. • Drive platform modernization initiatives such as Delta Lake and metadata‑driven design. • Create reusable frameworks and platform capabilities to accelerate analytics, ML, and governed self‑service data access. • Lead root‑cause analysis for major data issues and implement long‑term improvements in data quality, lineage, and observability. • Provide technical leadership, guidance, and mentorship to Staff, Senior, and mid‑level data engineers. • Influence cross‑organizational roadmaps and engineering investments; participate in architecture reviews and governance forums.