EXANTE

Architect

• Design, maintain, and continuously improve high-availability, low-latency trading backend infrastructure with a focus on uptime, throughput, and operational resilience
• Partner with product owners to gather and interpret business requirements, translate them into clear technical specifications, and lead initiatives from design through production release
• Collaborate closely with the management team to align architectural direction with long-term product strategy and organisational goals
• Drive the decomposition of monolithic systems into maintainable, independently deployable microservices, ensuring a smooth transition with minimal disruption
• Define and champion engineering standards, architecture review processes, and best practices across teams

System Architect

Infrastructure Engineer

• Operate and maintain on-premises infrastructure based on bare-metal Debian Linux servers
• Manage OS-level configuration, networking, and system services
• Automate infrastructure provisioning and lifecycle management using Chef
• Manage infrastructure state via Git repositories following GitOps principles
• Standardize and continuously improve infrastructure deployment workflows
• Manage and operate hybrid connectivity across GCP, AWS, Megaport, and on-prem data centers
• Design and maintain VPC networking, routing, and firewall rules
• Operate hybrid connectivity (Direct Connect, Cloud Interconnect, VPN)
• Configure and support BGP routing between cloud and on-prem environments
• Ensure high availability, redundancy, and fault tolerance of network connectivity
• Troubleshoot network and connectivity issues across cloud and on-prem layers

Senior Infrastructure Engineer

QA Engineer

• Define and maintain a comprehensive framework of best-in-class recruitment practices covering sourcing, screening, interviewing, offer management, and onboarding
• Benchmark against industry leaders in talent acquisition across financial services and technology sectors
• Stay current on emerging recruitment methodologies, assessment tools, and AI-driven sourcing and screening technologies
• Conduct ongoing audits of recruitment processes, candidate experience, hiring manager engagement, and TA team performance
• Evaluate ATS utilisation, sourcing channel effectiveness, interview quality, and hiring decision consistency
• Identify bottlenecks, inefficiencies, and gaps in the hiring funnel
• Establish and track TA KPIs: time-to-fill, quality of hire, source effectiveness, candidate experience, offer acceptance rate, and others
• Deliver candid, data-driven assessments of TA operations to leadership with actionable recommendations
• Drive measurable improvements — this role is judged on what changes, not the reports

Talent Acquisition QA Controller

Data Scientist

• Define and build predictive models from scratch, starting with:
• Work with raw trading, transactional, and behavioral data from our data warehouse
• Define target variables and operationalize business concepts (e.g., what constitutes "churn" in a brokerage context) into measurable ML targets
• Engineer features from client activity, trading patterns, market conditions, and engagement signals
• Select, train, validate, and iterate on models — starting simple, increasing complexity where it earns its keep
• Design monitoring for model performance, data drift, and degradation over time
• Deliver daily client-level scores that integrate into CRM workflows and sales processes
• Translate model outputs into actionable insights for non-technical sales managers
• Work with sales leadership to design interventions around model predictions
• Present results, assumptions, limitations, and recommendations to senior stakeholders

Data Engineer

• Define how data is understood, accessed, and used by AI systems across the company.
• Build clean, reusable analytical data layers in BigQuery.
• Move business logic from BI into the warehouse.
• Define metrics, dimensions, and semantic consistency.
• Enable reliable AI ↔ data interaction.
• Design schemas + instructions so AI produces correct outputs.
• Test and refine real AI usage (not theory).
• Implement data-layer access control (not BI-layer).
• Ensure consistent behavior across BI, AI, and internal tools.
• Ensure metric reconciliation across different data sources.
• Prevent sensitive data leakage and uncontrolled query cost.
• Replace manual data workflows with AI-driven processes.
• Build agents for reporting, validation, and internal analytics.

Senior Data Engineer

Financial Planning and Analysis

• Lead the end-to-end implementation of a budgeting tool, from requirements definition to go-live
• Manage and execute the transition from Excel-based budgeting templates to a budgeting software solution
• Design and implement scalable budgeting process aligned with business needs
• Work closely with Finance, HR, and key business stakeholders to gather requirements and align on solutions
• Configure budgeting models, assumptions, workflows, and reporting within the selected tool
• Ensure data accuracy, consistency, and transparency throughout the budgeting process
• Support change management, including user training and documentation
• Act as the main point of contact between business stakeholders and technical or system vendors (if applicable)

Budgeting Tool Implementation Lead

Backend Engineer

• Design, develop and implement new features from idea stage all the way into production
• Maintain current functionality against the backdrop of growing number of data and requests
• Participate in architecture development, writing and maintaining documentation
• Actively interact with your team, code reviews, mentorship and knowledge sharing

System Architect

Senior Infrastructure Engineer

Talent Acquisition QA Controller

Data Scientist

Senior Data Engineer

Budgeting Tool Implementation Lead

Budgeting Tool Implementation Lead

Budgeting Tool Implementation Lead

Erlang Developer