• Wspieraniu klientów w tematyce inżynierii i analityki dużych zbiorów danych w środowisku chmurowym – tworzenie rozwiązań od koncepcji po wdrożenie oraz inne fazy procesu SDLC/DDLC • Byciu ekspertem technicznym – definiowaniu rozwiązań Data Intelligence i zarządzaniu ich wdrażaniem pod kątem technicznym i metodycznym • Wspieraniu celów biznesowych naszych klientów poprzez opracowywanie i wdrażanie rozwiązań analitycznych • Budowaniu Proof of Concept (PoC) i nadzorowaniu architektury Microsoft dla naszych klientów • Dzieleniu się wiedzą i byciu trenerem technicznym w zakresie Azure Data Intelligence, oferowaniu wsparcia pracownikom, związanego z tematyką BI i analityką danych • Współpracy z innymi działami biznesowymi w celu dostarczania przydatnych rozwiązań analitycznych • Proponowaniu nowych możliwości wykorzystania danych, wdrażaniu nowoczesnych rozwiązań analitycznych oraz dbaniu o najwyższą jakość analiz i raportów, które pomagają kształtować strategiczne decyzje biznesowe

• You will partner with Risk, Fraud and Disputes Engineers and Product Managers to deeply understand business needs and translate them into actionable data-driven solutions • You will lead the creation of high-quality labeled datasets that power supervised machine learning, rules-based fraud detection and predictive chargeback modeling • You will collaborate closely with Machine Learning and Backend Teams to productionize data assets and enable scalable, reliable decisioning systems • You will analyze large-scale transactional, fraud and chargeback datasets to surface trends, detect anomalies and support deep-dive investigations (e.g., Chargeback win/loss rates, Fraud rates, anomalous Merchant behaviour etc.) • You will build and maintain real-time dashboards and reporting mechanisms that provide leadership, operations and product teams with visibility into critical KPIs across Risk, Fraud and Disputes • You will identify and recommend data improvements to strengthen fraud and chargeback detection capabilities and reduce false positives and false negatives • You will act as a domain expert on fraud and dispute data, supporting internal investigations and responding to customer-facing data queries with clarity and accuracy • You will partner with Finance and Operations to answer business-critical questions related to dispute performance, costs and trends • You will collaborate with Engineering to design, validate and maintain Airflow pipelines and ETL workflows that support scalable and resilient data infrastructure

• Build, operate, and monitor Cribl’s core data tech stack including data pipelines, data integrations and our data warehouse ensuring data is accurate, timely, and trusted • Develop cloud-native services and infrastructure that power scalable, reliable data systems, with logging, alerting, and observability as first-class concerns • Contribute to infrastructure-as-code (Terraform or similar), clean deployment patterns, and operational hygiene • Support Cribl’s growing data science and agentic initiatives by preparing model-ready datasets, exposing features, and integrating AI/LLM workflows into production systems • Work closely with Analysts and business stakeholders to clarify requirements, validate data outputs, and translate business logic into reliable data artifacts • Partner closely with Data Analysts, Site Reliability Engineers, and IT Engineers on initiatives that align with business needs to clarify requirements and validate data outputs • Communicate risks, tradeoffs, and timelines proactively to keep work predictable • Contribute to secure, compliance-minded engineering practices in collaboration with IT/Security

• Evaluate and restructure existing data architecture to support scalability and performance • Design new schemas, relationships, and data models that align with business logic and analytics needs • Build and maintain a HelpGrid-centric data layer that consolidates fragmented sources into a central structure • Provide strategic guidance on how data should be organized, named, and modeled for long-term sustainability • Establish best practices for schema versioning, documentation, and change control • Design and implement the company’s first ETL framework, defining how data is extracted, transformed, and loaded from multiple sources • Build automated, reliable pipelines that move data from the centralized database and external tools into analytics-ready structures • Standardize transformation logic to clean, normalize, and enrich data for business use • Implement pipeline monitoring, error handling, and validation for data quality assurance • Provide architectural and workflow recommendations for how data should flow between systems and teams • Define how analysts should access, refresh, and use data safely and consistently • Partner with the Data & Analytics Manager to align the engineering roadmap with BI and reporting priorities • Develop scalable, reusable scripts and frameworks that simplify ongoing data management • Integrate data from internal and third-party platforms into a centralized environment • Optimize query and pipeline performance for high-volume operations • Build APIs or microservices for data synchronization and access • Document data lineage, schema definitions, and system dependencies • Implement data access controls, validation checks, and compliance standards • Maintain transparent documentation for analysts, developers, and leadership • Promote data stewardship and governance best practices across departments