[GVI] Senior Software Engineer (Python + Cloud + Data)

Software Mind develops solutions that make an impact for companies around the globe. Tech giants & unicorns, transformative projects, emerging technologies and limitless opportunities characterize their average day.

• Around 4–7 years of hands-on commercial software engineering experience
• Strong production Python skills across services and data pipelines, including testing and CI/CD
• In-depth cloud experience (compute, managed databases, serverless, IAM), ideally on GCP, though AWS or Azure experience also counts
• Full-stack range, strongest on APIs and the middle layer, with the ability to adjust a React/TypeScript front end without a designer
• Data engineering experience: ingestion from APIs, databases and file extracts, orchestration (Airflow preferred) and schema design for SQL and ideally NoSQL databases
• Proven experience taking prototypes or AI-generated code to production and owning a service end to end, including deployment, monitoring and documentation
• Hands-on experience securing your own services: authentication, least-privilege IAM, secrets handling, input validation, audit logging, dependency hygiene, and secure document storage and retention
• Autonomy and commercial awareness: handling unclear briefs without hand-holding, fast comprehension of wide, undocumented tables and unfamiliar systems, and anticipating user needs

Nice to have:

• Experience building integrations with Salesforce or planning platforms such as Pigment and Anaplan
• Knowledge of canonical or dimensional data modelling, and interest in contributing to a company-wide data strategy
• Exposure to LLM workflows and prompt evaluation
• Experience with Lovable or Supabase

• Turn internal apps prototyped in Lovable into secure, production-grade services on Cloud Run
• Build backends and middle layers with authentication, audit trails, and secure document storage
• Build and orchestrate data feeds connecting business systems such as Salesforce and third-party data providers to the canonical data layer, dashboards, and planning tools like Pigment and Anaplan
• Automate recurring reporting and analysis, including scheduled data centralisation and quarterly metrics with manual overlays
• Own the security of everything you ship, since there is no separate InfoSec function
• Provide technical review of AI Skills used by business teams
• Document your work as you go (READMEs, runbooks, architecture notes) so others can maintain it
• Explain trade-offs to non-technical stakeholders, flag technical risks and sequencing concerns to the sponsor, and decline scope that belongs to other teams