Senior Platform and Application Engineer

ABOUT THE OPPORTUNITY

Join the Portuguese engineering team of an AI- and automation-driven financial crime operations company from Iceland as a Senior Platform and Application Engineer and help transform how financial institutions fight money laundering and financial crime globally. 

You'll be working for an Icelandic technology company headquartered in Reykjavík, building an Azure-native platform that combines scalable data engineering, advanced agentic AI systems, and workflow automation to make AML (Anti-Money Laundering) operations faster, more accurate, and significantly more cost-efficient. The company is moving beyond traditional software-as-a-tool toward delivering automation, intelligent agents, and operational outcomes that fundamentally transform financial crime operations.

This is a unique opportunity to join during a critical growth phase as the engineering team expands from 10 to approximately 15 experienced engineers under unified leadership. The company maintains a rigorous design-first engineering culture where architectural decisions are documented, reviewed, and debated before implementation through design documents, RFCs, and structured discussions. You'll work across legacy boundaries handling backend services, API design, distributed data pipelines, reliability engineering, and integration layers, solving complex technical challenges in the financial crime domain.

Critical Requirements: This is a senior-level position requiring deep understanding of distributed computation and data pipelines, ability to reason mathematically about performance and algorithmic complexity, experience with large-scale analytics engines, strong system-level thinking, and proven capability designing deterministic high-performance pipelines. The company prioritizes engineering fundamentals, versatility, and adaptability over narrow tool specialization. B2 English is essential. Banking or fintech platform engineering experience is highly valued.

PROJECT & CONTEXT

You'll be building the technological core of an operational transformation in financial crime fighting, working on an Azure-native platform that powers Anti-Money Laundering operations for financial institutions worldwide. The platform combines multiple sophisticated capabilities including agentic AI systems that support and automate case decisions, precomputed analytics and detection engines that identify suspicious patterns, workflow automation that streamlines AML operations, and human-in-the-loop intelligence systems that reduce manual workload while maintaining oversight and accuracy.

Your work centers on distributed computation and data pipelines processing high volumes of financial transaction data, customer information, and risk signals at scale. You'll design and implement deterministic, high-performance pipelines that must operate reliably under real-world conditions, handling data consistency, fault tolerance, and performance requirements critical for financial crime detection. Understanding how to reason mathematically about performance and algorithmic complexity ensures systems scale efficiently as data volumes and customer bases grow.

Large-scale analytics engines form the backbone of the detection capabilities - you'll work with distributed computation frameworks processing massive datasets, implementing complex analytical logic that identifies money laundering patterns, and ensuring analytics run with predictable performance characteristics. Your experience with systems like Spark, distributed data processing, and high-volume analytics enables you to build the computational infrastructure that powers intelligent financial crime detection.

The engineering culture emphasizes design before implementation - you'll participate actively in creating design documents, reviewing architectural proposals, and engaging in structured technical debates that ensure transparency and consistency across the platform. This rigorous approach means architectural decisions are documented and scrutinized before coding begins, requiring ability to articulate technical reasoning, evaluate trade-offs, and build consensus around design choices.

System-level thinking distinguishes this role from typical backend development - you'll understand how components interact across data ingestion, compute layers, storage systems, API services, and operational workflows. This holistic perspective enables you to identify bottlenecks, design effective integration patterns, and ensure the platform operates cohesively. You'll work across multiple domains rather than being siloed in a single area, requiring versatility and broad technical capability.

Reliability and maintainability are non-negotiable - you'll design systems that operate predictably under production conditions, implement observability and monitoring that provides operational awareness, and build in fault tolerance and graceful degradation. Experience operating and debugging production systems informs your designs, ensuring you create solutions that work in the real world, not just in ideal conditions. Understanding distributed service interactions, debugging production issues, and maintaining system health are daily responsibilities.

Measured pragmatism guides technology choices - the company values engineers who choose technologies that solve real problems rather than adopting tools for their own sake. You'll evaluate trade-offs between different approaches, select appropriate technologies based on requirements rather than trends, and build solutions that balance innovation with proven reliability. While the company uses Azure, AKS, Spark, Iceberg, Trino, Redis, Keycloak, and React, they seek engineers who think conceptually, adapt quickly, and navigate multiple technology environments.

Ownership and accountability define the engineering mindset - you'll drive outcomes across design, implementation, and operations, taking full responsibility for the systems you build. This includes ensuring code quality, maintaining production systems, responding to operational issues, and continuously improving platform capabilities. The role demands engineers who can work independently while collaborating effectively with the unified engineering team.

Your work directly impacts the platform's ability to enable significantly lower cost-to-serve for AML operations while improving detection accuracy and reducing false positives. Understanding the financial crime domain, regulatory requirements, and operational constraints helps you build solutions that deliver real business value for financial institutions fighting money laundering.

Core Tech Stack: Azure (cloud platform), Spark (distributed processing), AKS (Kubernetes), Iceberg (data lakehouse), Trino (query engine), Redis (caching), Keycloak (identity), React (frontend)

Engineering Focus: Distributed systems, data pipelines, performance engineering, API design, system reliability, observability

Domain: Financial crime operations, Anti-Money Laundering (AML), transaction monitoring, risk detection

Culture: Design-first, architectural rigor, system-level thinking, measured pragmatism, ownership-driven

Scale: Global financial institutions, high-volume transaction processing, mission-critical detection systems

Team: Growing from 10 to ~15 senior engineers under unified VP Engineering leadership, target full staffing by end of Q1 2026

WHAT WE'RE LOOKING FOR (Required)

Distributed Systems Expertise: Deep understanding of distributed computation, distributed data processing, service coordination, consistency patterns, and distributed systems challenges - this is the core technical requirement

Data Pipeline Engineering: Strong experience designing and implementing data pipelines for high-volume data processing, ETL/ELT workflows, data transformation, and pipeline orchestration at scale

Mathematical Reasoning: Ability to reason mathematically about performance characteristics, algorithmic complexity (Big O notation), scalability implications, and optimization trade-offs

Large-Scale Analytics: Experience with large-scale analytics engines for processing massive datasets, distributed computation frameworks, and analytical workload optimization

Performance Engineering: Skilled in designing deterministic, high-performance systems with predictable behavior, understanding performance bottlenecks, and implementing optimization strategies

System-Level Thinking: Understanding of how components interact across distributed systems, ability to reason about system architecture holistically, and awareness of cross-cutting concerns

API Design Excellence: Strong API design and service reliability background including RESTful principles, API versioning, contract design, and building production-grade APIs

Production Systems Experience: Demonstrated experience operating and debugging production systems, understanding operational requirements, and maintaining system reliability

Reliability Engineering: Knowledge of reliability patterns, fault tolerance, graceful degradation, circuit breakers, retry logic, and designing for failure scenarios

Maintainability Focus: Ability to design systems for long-term maintainability including clear architecture, comprehensive documentation, and operational supportability

Cloud-Native Architecture: Experience with cloud-native architectures, containerization, orchestration, and building applications designed for cloud environments

Cross-Layer Reasoning: Ability to reason across application and infrastructure layers, understanding how software interacts with underlying infrastructure and vice versa

Observability & Debugging: Deep understanding of distributed service interactions, observability practices (metrics, logging, tracing), and systematic debugging of complex systems

Architectural Ownership: Strong problem-solving capabilities with ability to take architectural ownership, drive technical decisions, and own outcomes from design through operations

Design Documentation: Experience creating design documents, architectural proposals, and technical specifications that communicate decisions clearly

Pragmatic Technology Choices: Measured pragmatism in technology selection, evaluating solutions based on requirements and trade-offs rather than trends or preferences

Ownership Mindset: Strong sense of ownership and accountability for engineering outcomes, taking responsibility across the full lifecycle from design to production operations

Multi-Stack Capability: Ability to work across multiple technology stacks when needed, learning new tools and frameworks quickly, and adapting to evolving technical landscapes

Communication Skills: Effective communication for participating in architectural debates, design reviews, and cross-functional collaboration

English Proficiency: B2 level (Upper Intermediate) or higher in English for design documentation, technical discussions, and team collaboration

Work Authorization: Eligibility to work from Lisbon, Portugal with availability for hybrid work model (1-3 days per week in office)

NICE TO HAVE (Preferred)

Azure Cloud Platform: Hands-on experience with Microsoft Azure including core services, Azure-specific patterns, and cloud resource management

Apache Spark: Production experience with Apache Spark for distributed data processing, Spark SQL, DataFrames, and performance tuning

Kubernetes & AKS: Experience with Kubernetes container orchestration, specifically Azure Kubernetes Service (AKS), including deployment, scaling, and operations

Apache Iceberg: Knowledge of Apache Iceberg table format for data lakehouse architectures, table management, and schema evolution

Trino (Presto): Experience with Trino distributed SQL query engine for interactive analytics across multiple data sources

Redis: Hands-on experience with Redis for caching, session management, real-time data structures, and performance optimization

Keycloak: Familiarity with Keycloak for identity and access management, SSO implementation, and authentication/authorization

React: Frontend development experience with React for building user interfaces and understanding full-stack implications

Event-Driven Architecture: Experience with event-driven and message-oriented systems including event streaming, pub-sub patterns, and asynchronous processing

Message Queues: Knowledge of message queue systems like Azure Service Bus, Kafka, RabbitMQ, or similar technologies

Data Modeling: Strong data modeling skills for both relational and non-SQL databases, schema design, and data architecture

Banking/Fintech Domain: Previous experience in banking, fintech, or financial services with understanding of financial data, regulatory requirements, and compliance constraints

AML/Financial Crime: Specific experience with Anti-Money Laundering systems, transaction monitoring, financial crime detection, or regulatory compliance systems

Platform Engineering: Experience building internal platforms, developer tooling, or infrastructure that other teams build upon

High-Volume Processing: Experience with systems processing millions or billions of records, understanding throughput optimization and resource management

Stream Processing: Knowledge of stream processing frameworks for real-time data processing and event handling

Data Quality: Experience implementing data quality frameworks, validation rules, and data governance practices

SQL Expertise: Advanced SQL skills including query optimization, complex analytical queries, and database performance tuning

Python Development: Strong Python programming for data engineering, automation, scripting, and backend services

JVM Languages: Experience with JVM languages like Java or Scala, particularly for distributed computing frameworks

Infrastructure as Code: Experience with Terraform, ARM templates, or other IaC tools for infrastructure provisioning

CI/CD Pipelines: Hands-on experience building and maintaining CI/CD pipelines for automated testing and deployment

Monitoring Solutions: Experience with monitoring and observability platforms like Prometheus, Grafana, Application Insights, or ELK Stack

Service Mesh: Knowledge of service mesh technologies for microservices communication, traffic management, and observability

GraphQL: Experience with GraphQL for API design and implementation as alternative or complement to REST

gRPC: Knowledge of gRPC for high-performance service-to-service communication

Batch Processing: Experience with batch processing frameworks and orchestration tools like Airflow, Azure Data Factory, or similar

Real-Time Analytics: Experience building real-time analytics systems, dashboards, and operational intelligence

Security Practices: Understanding of security best practices in distributed systems, data encryption, secure communication, and compliance

Disaster Recovery: Experience designing and implementing disaster recovery strategies and business continuity plans

Cost Optimization: Skills in cloud cost optimization, resource efficiency, and designing cost-effective architectures

Technical Leadership: Experience mentoring engineers, leading technical initiatives, or architectural leadership roles

Agile Practices: Experience working in Agile environments with understanding of how architecture fits within iterative delivery

Regulatory Compliance: Familiarity with financial services regulations like GDPR, AML directives, KYC requirements, or similar compliance frameworks

Location: Lisbon, Portugal (Hybrid - 1-3 days per week in office)