Description
**About the Role**:
We're looking for a **mid-level Decisions Engineer** to join our Data Engineering team and take ownership of our core Credit & Fraud Decision Engines.
In this crucial role, you'll be the bridge between our business, data, and technology teams, translating complex business logic into efficient, scalable, and reliable decision-making systems.
You'll ensure our engines are fast, accurate, and robust enough to handle high-stakes, real-time decisions, directly impacting our business's ability to manage risk and provide a seamless customer experience.
**What You'll Do**:
- ** Own the Decision Engine**: Design, build, deploy, and optimize our credit and fraud decision engines from end to end.
You'll be the primary owner of these systems, ensuring their performance, reliability, and accuracy.
- ** Map Data and Logic Flows**: Deeply understand the flow of data from source systems, through data engineering pipelines and ML models, to the final decisioning logic.
You'll be responsible for mapping this entire journey to ensure data integrity and system coherence.
- ** Translate Business Requirements**: Work closely with business and product teams to translate complex credit and fraud policies into well-structured, maintainable decision rules and system configurations.
- ** Collaborate on Integrations**: Partner with our engineering teams to design and build APIs and integrations that facilitate real-time data exchange with our decision engines and other critical systems.
Transversal view on identifying quality, performance or scalability challenges are critical even if it affects other areas, collaboration, visibility,



RCA analysis and continuos improvement is key.
- ** Deploy and Monitor Models**: Collaborate with our data science team to integrate, deploy, and monitor machine learning models within the decisioning framework, ensuring they align with and enhance our decision logic.
- ** Ensure System Health and Compliance**: Proactively monitor system performance, troubleshoot issues, and ensure our decision systems comply with all relevant industry standards and regulations.
Development quality and observability is key, you will be responsible for decision engine performance, system health, scalability and well-designed architectural decisions.
**What We're Looking For**:
**Required Qualifications**:
- ** Experience with Decision Systems**: You have a strong background working with decision engines, rules engines, or other complex business logic frameworks.
- ** Technical Acumen**: A solid grasp of data modeling, APIs, and system integrations.
You're comfortable with both SQL and a scripting language like **Python**.
- ** Cross-Functional Collaboration**: You're a skilled communicator who can work effectively with diverse teams, from data engineering and data science to product and compliance.
- ** Problem-Solving Skills**: You possess a strong ability to identify problems, analyze data,



and propose solutions in a detail-oriented, high-stakes environment.
- **Languages & Frameworks**: Python, Typescript, Java, and shell scripting.
- ** Cloud Platforms**: Google Cloud Platform (GCP) and AWS, with a focus on GKE (Standard), Cloud Functions, and AWS Lambda.
- ** Containers & Orchestration**: Kubernetes (specifically GKE Standard) and Istio.
- ** Databases**: Relational databases, including RDS for MySQL.
MongoDB and Dynamo are also recommended.
- ** Observability & Monitoring**:
- ** Metrics & Dashboards**: Prometheus and Grafana on Cloud.
- ** Logging & Analysis**: Cloud Logging and Kibana/Elasticsearch.
- ** Incident Management**: On-call suites (e.g., Grafana On-Call, Better Stack), post-mortem analysis, and incident response.
- ** Decision Systems**: Taktile (or similar decision/rules engines).
- ** Problem-Solving**: Root Cause Analysis (RCA), and implementing architectural and code-based solutions for scalability and performance.
- ** Data Flow Knowledge**: You have an understanding of data pipelines and the lifecycle of data from ingestion to consumption.
**Preferred Qualifications**:
- ** Industry Experience**: Experience working in the financial services or e-commerce industry, particularly with credit, fraud, or risk management systems.
- ** Cloud Platforms**: Familiarity with cloud services on AWS, GCP, or Azure.
- ** Data Orchestration**: Experience with data orchestration tools like Airflow or similar platforms.
**MLOps**: A basic understanding of machine learning models and the MLOps lifecycle.

📌 Decisions Engineer (Xico)
🏢 Bankaya
📍 Xico