• Design, code, and test software features and components. • Collaborate with cross-functional teams to gather and refine software requirements. • Lead coding efforts for specific modules or features. • Troubleshoot and resolve complex software issues. • Participate in code reviews and enforce coding standards. • Contribute to architectural decisions and design discussions.

• Select and own the stack for the Music & Audio vertical: CDP, product analytics, lifecycle/messaging, experimentation, warehouse, reverse-ETL, and entitlements — including writing defensible recommendations with cost model and tradeoffs • Design event taxonomy with consistent schema and identity resolution across anonymous → free → paid → multi-device • Build and attribute the full growth funnel across paid, organic, partner, and in-product virality • Architect in-app PLG motion: viral loops, referral mechanics, upgrade prompts, and sharing hooks • Define and execute lifecycle marketing strategy: segmentation, behavioral triggers, and messaging cadences across activation, habit, and expansion • Ship free-to-paid logic: trial mechanics, paywalls, in-app messaging, and entitlement checks across web and native runtimes • Build onboarding, churn-risk, reactivation, and win-back sequences • Instrument A/B experimentation with exposure logging and statistical guardrails • Own the growth dashboard: activation, conversion, ARPU, LTV, retention cohorts, and expansion rate

• Lead the assessment, design, and implementation of normalized data architectures across SQL Server and PostgreSQL environments. • Define and enforce enterprise data modeling standards, schema governance, naming conventions, and referential integrity practices. • Develop and maintain canonical data models, ER diagrams, and data dictionaries. • Identify and remediate legacy schema issues, driving migration to scalable and maintainable database structures. • Establish indexing strategies, query optimization standards, and performance tuning best practices. • Collaborate with Software Architects and engineering teams to align data architecture with application and integration designs. • Design and enforce multi-tenant data isolation strategies across SaaS platforms. • Integrate security, compliance, and governance requirements into database and schema designs. • Implement and manage database change management processes, schema versioning, and CI/CD-driven deployments. • Analyze application and business data access patterns to support scalable, low-latency architectures. • Evaluate and recommend tools, frameworks, and best practices for schema management and database modernization. • Support microservices and API-driven architectures through robust data layer design. • Provide technical leadership and mentorship on database design and data modeling best practices. • Ensure scalability, maintainability, performance, and reliability of enterprise database systems. • Drive continuous improvement initiatives in data architecture, governance, and engineering practices.

• Our ideal candidate will have 20% of the time influencing junior/mid-level engineers and 80% focused on complex problems. • This candidate considers themselves among the highly technical and likes to push the limits of their knowledge and experience every day. • In this role, you will work on designing, implementing, scaling, and maintaining the code that powers our production systems. • You will work with no supervision, ensuring timely and high-quality delivery. • You will communicate and work closely across 3 teams (Outcomes, Methodology & Recruitment) and with related areas like product and data science. • With great autonomy and freedom to innovate, you will play a key role in world-class research building the next generation of Cint products. • Design, implement, scale, and maintain backend systems that process large volumes of data. • Work on event-driven and API-based integrations. • Contribute to migration efforts toward a more domain-based and scalable architecture. • Build and optimize software for performance-sensitive workflows. • Investigate technical problems and propose solutions together with the team. • Collaborate closely with data science and engineering colleagues on technical solutions. • Participate in a collaborative development model where work is shared rather than handled alone.