Traffic Label Limited

• Develop and implement retention strategies to maximize customer retention, engagement, and customer lifetime value. • Plan, build, and execute email, SMS, and other CRM campaigns in line with brand and product roadmaps. • Set up, execute, and report on A/B tests across CRM channels to continuously optimize campaign performance. • Build and manage landing pages and on-site campaigns using CMS and landing page builder platforms across desktop and mobile. • Execute promotional campaigns and bonus payouts through our iGaming platform. • Collaborate with Marketing, Brand, Design, Product, and Content teams to ensure CRM initiatives align with overall business objectives. • Monitor customer behaviour, campaign performance, and retention metrics, providing actionable insights and recommendations. • Stay up to date with industry trends and identify opportunities to improve the customer experience. • Write engaging and high-quality copy for emails, SMS, landing pages, and other CRM communications. • Proofread CRM content to ensure accuracy, consistency, and quality. • Conduct research to ensure content remains relevant, engaging, and aligned with market trends. • Manage CRM calendars and support the planning of future CRM strategies. • Ensure timely delivery and flawless execution of all CRM initiatives. • Prepare regular performance reports and present key insights to senior management. • Support additional CRM and retention-related projects as required.

• Design, implement, and maintain scalable Kubernetes infrastructure on GKE/EKS • Develop and manage Infrastructure as Code using Terraform, Helm, and Ansible • Build and improve CI/CD pipelines for fast and reliable deployments • Implement and maintain monitoring, logging, and alerting solutions • Support PostgreSQL and Kafka environments • Automate operational tasks using Python and Bash scripting • Troubleshoot production issues across cloud and Kubernetes environments • Collaborate with developers to improve deployment and operational processes • Participate in on-call rotation and production support

• Design and build automated workflows - both deterministic (rule-based) and probabilistic (model-driven) • Implement agent orchestration patterns (planning, tool use, evaluation loops, error recovery) • Build and maintain APIs and backend services supporting AI-driven functionality • Develop retrieval and grounding systems for knowledge search and context enrichment • Work with structured and unstructured data to support AI use cases • Design evaluation frameworks to measure and monitor AI system quality • Design testing strategies for non-deterministic AI systems • Manage cost, latency, and quality tradeoffs in production AI systems • Collaborate with Backend, Data, DevOps, and Product teams • Deploy, monitor, and maintain services in production environments • Contribute to system design and technical decision-making

• Design, implement, and maintain scalable Kubernetes infrastructure on GKE/EKS • Develop and manage Infrastructure as Code using Terraform, Helm, and Ansible • Build and improve CI/CD pipelines for fast and reliable deployments • Implement and maintain monitoring, logging, and alerting solutions • Support PostgreSQL and Kafka environments • Automate operational tasks using Python and Bash scripting • Troubleshoot production issues across cloud and Kubernetes environments • Collaborate with developers to improve deployment and operational processes • Participate in on-call rotation and production support

• Build and maintain ingestion pipelines (REST APIs, SFTP, database replication) into Snowflake • Write dbt models across bronze/silver/gold layers following established conventions • Integrate with third-party ESPs (DotDigital, Adestra, FastTrack): push segments, pull back campaign events (opens, clicks, bounces, conversions) • Develop and maintain AWS Lambda functions for data extraction and reverse ETL • Build data models for identity resolution, segmentation, and attribution • Configure and manage Airflow DAGs for orchestration • Implement data quality checks, monitoring, and alerting • Manage Snowflake and AWS infrastructure via Terraform • Investigate and resolve data incidents (pipeline failures, data quality issues, PII exposure)

• Develop and maintain backend services for real-time event processing and data ingestion from multiple sources • Build and enhance REST APIs for affiliate dashboards, admin panels, and reporting • Implement financial logic including revenue share, CPA tracking, and multi-currency calculations • Integrate with external gaming platforms and third-party systems • Develop and maintain React-based frontends, including internal tools and affiliate portals • Work with event-driven architecture (e.g. Kafka) for scalable data processing • Collaborate with cross-functional teams to deliver high-quality features • Improve existing codebase and contribute to system scalability and performance

• Design, develop, and maintain integrations between third-party platforms and internal systems • Build scalable backend services and API-based integration solutions using Python • Manage integrations across AWS server-less infrastructure including Lambda, S3, DynamoDB, EventBridge, and related services • Implement and maintain authentication flows, token refresh handling, retries, pagination, polling, and async job processing • Monitor, troubleshoot, and resolve integration issues in production environments • Own integrations end-to-end, including implementation, deployment, operations, and incident handling • Write and maintain automated tests to ensure reliability and stability of integration services • Collaborate closely with Engineering, Product, and Data teams to support platform scalability and operational efficiency • Contribute to infrastructure improvements and Infrastructure-as-Code initiatives.

• You sit between the business (Product Owner, stakeholders) and the engineering team. • About 30% of your time is practical data work: validating that CDP capabilities behave as specified, exploring data before specs are written, testing hypotheses for new capabilities, building shared gold layer models that consumer teams use, and helping those teams understand what data is available. • It is not business reporting or analytics built for individual brands. • The other 70% is Systems Analysis: turning what you find into specs engineers can build from, owning data quality from symptom to root cause, raising what isn’t right before it ships, and shaping the semantic layer so other business lines can find and trust the data. • Four things define the systems analysis side: Turn business intent into specs that engineers can build from. Talk to stakeholders, figure out what they actually need, and write it down. Each spec covers source schema, target model, transformation logic, business glossary, schedule, data quality checks, consumer, and use case. • Stay reachable through development. Engineers will have questions. Find and fix data quality problems at the root, alongside the engineering function. • Trace lineage, identify the cause, work with the data source owner on the fix, check downstream impact, and close the loop. • Confirm what’s delivered does what we said it would. Validate against the spec you wrote. When the data isn’t right, you raise it before it ships. • Make data usable beyond the team that asked for it. Contribute to the semantic layer, data descriptions, and shared definitions so other business lines can find what they need, understand what it means, and trust it without going through you each time. • You work on the Data Platform layer: CDP build, identity resolution, data quality, semantic definitions, and governance. Business analytics on top of the platform, such as dashboards and performance reports for specific brands, is produced by analyst teams aligned to those brands. Your job is to make sure those teams can do theirs: trustworthy data, clear definitions, fast issue resolution.

• Design and build ingestion pipelines for REST API, database sources, etc., using Python. • Own ETL and Reverse ETL end-to-end, including implementation, deployment, operations, and incident handling. • Understand how external systems work (casino platforms, ESPs, affiliate tools) to build reliable integrations with them. • Design DAG patterns: dependency management, partial retry, backfill procedures. • Implement and maintain authentication flows, token refresh handling, and secrets management. • Monitor, troubleshoot, and resolve integration issues in production environments. • Write and maintain automated tests to ensure the reliability and stability of integration services. • Collaborate closely with Engineering, Product, and Data teams to support platform scalability and operational efficiency. • Contribute to infrastructure improvements and Infrastructure-as-Code initiatives. • Write Architecture Decision Records (ADRs) for significant design choices.

• Build scalable, testable backend solutions independently from requirements through to production deployment • Own services end-to-end and guide complex features through the full development lifecycle • Integrate third-party services according to business and technical requirements • Collaborate closely with Backend, Frontend, QA, and Infrastructure teams • Contribute to architecture discussions and help shape the technical roadmap • Mentor team members on best practices, troubleshooting, and system design • Monitor, profile, and optimise application and database performance • Design and execute high-load performance testing scenarios • Work on complex SQL tasks and performance optimisation using PostgreSQL • Handle large-scale datasets with a focus on scalability and reliability • Troubleshoot production issues using logs, metrics, alerts, and monitoring tools