Stellenbeschreibung

About Veem

Veem is revolutionizing international money transfers by offering a platform that is seamless, transparent, and scalable. It combines global payment processing, foreign exchange optimization, and embedded financial tools to empower businesses of all sizes to confidently expand and manage operations globally. Veem collaborates closely with customers to unlock new revenue opportunities and maximize business impact.

Why Join Veem

Contribute to the movement of billions in global business payments more efficiently
Grow your career in a rapidly expanding fintech and embedded finance industry
Take ownership of impactful projects and observe tangible results quickly
Collaborate extensively with various teams including Product, Sales, and Operations
Drive innovation and help shape the future of business-to-business payments

Role Overview

We are seeking a Data Engineer specialized in Business Intelligence (BI) and Reporting to lead the design, development, and scaling of the analytics infrastructure which supports operational, revenue, customer, and executive decisions. This hands-on individual contributor role integrates analytics engineering with BI responsibilities, focusing on data modeling, workflow automation, and AI-assisted reporting operations. It requires building efficient SQL/dbt models, maintaining reporting datasets, enhancing data quality and governance, streamlining reporting processes, and supporting AI-driven reporting innovations.

Primary Responsibilities

Develop and maintain scalable and clean SQL and dbt data models, marts, semantic layers, and reporting datasets
Organize, cleanse, and document complex data structures
Create dependable reporting frameworks for business users with improved data consistency, metric governance, and standardization
Design maintainable data transformations and reusable analytics layers
Manage production dashboards, routine reports, KPI bundles, and reporting workflows across business units
Collaborate with stakeholders to define key performance indicators, business logic, and reporting needs; ensure dashboard accuracy, reliability, and usability
Bolster self-service analytics capabilities for teams
Establish and oversee automated reporting workflows and monitoring systems
Support AI-powered agents related to reporting quality assurance, data validation, KPI creation, dashboard monitoring, reporting automation, metric documentation, and data freshness checks
Review automated outputs, implement QA and governance processes, and convert manual reports to scalable automated solutions
Implement data quality assurance, validation, monitoring, alerting, and maintain thorough documentation and standards for metrics and reporting
Enhance systems' observability and reliability; proactively troubleshoot discrepancies and data issues

Essential Qualifications

3 to 6 years of relevant experience in analytics engineering, BI engineering, reporting engineering, data analytics, data modeling, or reporting automation
Advanced proficiency in SQL
Proven experience with dbt for building SQL tables, data marts, semantic layers, reporting datasets, and transformation pipelines
Familiarity with BI tools such as Looker, Tableau, Power BI, Metabase, Sigma, Hex, Mode, or equivalents
Experience in managing dashboards and recurring reports in production
Strong skills in data quality assurance, monitoring, and automation of reporting workflows
Excellent documentation skills with strong quality assurance focus
Capability to independently own and manage reporting infrastructure and workflows

Preferred (Bonus) Skills

Experience in fintech, payment systems, or B2B SaaS environments
Handling data associated with HubSpot, CRM systems, revenue operations, customer success, payments, or transaction data
Expertise in KPI governance and metric definitions
Knowledge of data freshness monitoring and alerting frameworks
Experience with AI tools and workflow automation technologies such as OpenAI, Anthropic, n8n, AI agents, reporting bots, dashboard QA agents, and orchestration of workflows
Track record of automating manual reporting processes

Indicators of Success

Reliable, scalable, and trusted reporting systems in place
Consistent dashboards and KPI definitions across teams
Substantial automation of manual reporting tasks
Proactive detection and resolution of data quality problems
Robust AI-supported reporting workflows with stringent governance and quality assurance

Data Engineer, BI & Reporting