Helcim is searching for a Data Platform Manager to lead the team that builds and runs the data platform behind our mission to become the world’s most loved payments company.

As the Data Platform Manager within Helcim’s data centre of excellence, you will lead a team of Data Platform Developers (aka data engineers ;)) whose work has real impact across the business. The platform your team builds and runs processes money, manages risk, and puts AI to work for teams across Helcim, so its quality shows up directly in how Helcim performs.

Your team already builds with AI-assisted and agentic workflows. You will keep that momentum and set the standard for how it scales safely. Collaborating cross-functionally, you will set the platform’s direction, define its roadmap, and hold the bar on scalability, performance, and quality. You are an experienced data leader who thrives on real-world problems and builds resilient, scalable systems that last. You keep your team delivering for the business while protecting the long-term health of the platform. Qualifications & Experience

• 7+ years of experience in data engineering, analytics engineering, software engineering, or a similar role, with 2+ years leading a team.
• A proven record of leading teams that balance business delivery with operational excellence.
• Strong experience designing, building, and running production-grade data pipelines and platform infrastructure.
• Experience deploying data products, ML models, or AI capabilities into production.
• Experience working cross-functionally with technical and non-technical stakeholders and leading complex technical projects from discovery through delivery.
• Strong understanding of analytics engineering (data modeling, version control, testing, CI/CD, documentation), data warehousing, ELT, and data governance.
• Proficiency in SQL and Python, or related languages.
• Working knowledge of how AI agents and LLM applications consume data: tool interfaces, MCP, semantic layers, and access controls.
• Hands-on use of AI coding tools (such as Claude Code or Cursor) in your own engineering workflow.

Key Competencies

• You turn vague, complex data problems into clear milestones and execution plans.
• You make sound trade-offs and find platform risks, technical debt, and governance gaps before they scale.
• You multiply your team through coaching, code review, and clear documentation.
• You set guardrails for AI use. You know where agents speed the team up and where human review is non-negotiable in a regulated payments environment.
• You drive buy-in for platform roadmaps across leadership, risk, and compliance.

Bonus Points

• Our stack: BigQuery on Google Cloud Platform, dbt, and Airflow, with SQL and Python as our core languages.
• Agentic tooling: experience building agentic workflows, IDE specs and skills that automate pipeline development, or MCP servers that expose governed data to agents.
• Containers: hands-on Docker or Kubernetes for deploying and managing data applications and MLOps workflows.
• Streaming and observability: Kafka or similar streaming platforms, and observability tools such as Datadog.
• Self-service analytics: Metabase or other business intelligence applications.
• Domain: experience supporting risk, fraud, or ML use cases in payments or fintech.

• Lead and grow the Data Platform team, setting standards for design, code review, documentation, and delivery.
• Set the technical direction and roadmap for the platform: prioritize against business and risk needs, make architecture trade-offs, and represent those decisions to leadership, auditors, and compliance.
• Own the platform end to end: ELT and real-time pipelines, the warehouse, and self-service access to gold-standard data, with the monitoring and incident response that keep it reliable.
• Run the production systems within Data’s domain that the business depends on, including risk management and the systems that process money and feed our ad platforms.
• Build data products, services, and agents that put Helcim’s data to work across the company.
• Advance MLOps and AI Gateway infrastructure for production models, and extend agentic engineering practices across the team.
• Automate data governance for quality, privacy, and security, aligned with PCI-DSS and privacy requirements, including controls on how agents access data.