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

Software Mind develops solutions for global clients, including tech giants and unicorns. The company focuses on cross-functional engineering teams that handle transformative projects using emerging technologies.

• 4–7 years of 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; experience with AWS or Azure also counts
• Full-stack skills strongest on APIs and middle layer; ability to adjust a React/TypeScript frontend without a designer
• Data engineering experience: ingestion from APIs, databases, file extracts; orchestration preferably with Airflow; schema design for SQL and ideally NoSQL databases
• Proven ability to take prototypes or AI-generated code to production and own the service end to end including deployment, monitoring, and documentation
• Hands-on experience securing own services with best practices
• Autonomy and commercial awareness to handle unclear briefs and unfamiliar systems

Nice to have:

• Experience integrating with Salesforce, Pigment, or Anaplan
• Knowledge of canonical or dimensional data modelling
• 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 like Salesforce and third-party providers to canonical data layers, dashboards, and planning tools such as Pigment and Anaplan
• Automate recurring reporting and analysis including scheduled data centralisation and quarterly metrics with manual overlays
• Own the security of shipped services (authentication, IAM, secrets handling, input validation, audit logging, dependency hygiene, secure document storage and retention)
• Provide technical review of AI Skills used by business teams
• Document work continuously with READMEs, runbooks, and architecture notes
• Communicate trade-offs, technical risks, and scope limitations to stakeholders