Software Data Engineer

The role
We are a small company that runs its operations on internal software built with AI coding agents (Claude
Code). We have production web applications, databases, scheduled data syncs, and third-party API
integrations in daily use. We are hiring our first dedicated engineer to maintain, fix, and harden these
systems, and then to help build new ones. You will review AI-written code, keep integrations running when
vendor APIs change, and raise the quality bar on testing, deployment, and security.
Expectations
• Read and understand the existing codebase, specs, READMEs, and migration history before changing
anything.
• Diagnose and fix production failures, especially API syncs that break when a vendor changes
something.
• Review AI-generated pull requests line by line; test, challenge, and correct them.
• Build CI/CD, automated tests, and monitoring so failures are caught early and never silent.
• Harden database security, access control, and secrets management.
• Build new features, integrations, data pipelines, and reports.
Requirements
Required technical skills
• 5+ years of professional software engineering, with recent production work in TypeScript.
• PostgreSQL: schema design, migrations, views, SQL and PL/pgSQL functions, row level security, indexes, and reading query plans.
• Full-stack TypeScript: Node.js, React, and Next.js (App Router preferred), with strict typing.
• API integrations: REST and JSON APIs, authentication, pagination, rate limits, retries, idempotent syncs, and handling schema changes.
• Git and GitHub: branching, pull requests, code review, GitHub Actions.
• Testing and reliability: unit, integration, and database tests; logging, monitoring, and incident root- cause analysis.
• AI coding agents: daily use of Claude Code, Cursor, Codex, or similar on a real codebase, with a disciplined habit of reviewing their output.
• Data accuracy: comfortable with financial data (decimal and cents math, reconciliation, audit trails).
• Security basics: least-privilege access, secrets handling, authentication, and protecting personal data.
Important Tools
• Supabase (RLS, Edge Functions on Deno, pg_cron, Auth, CLI migrations) and Vercel.
• AWS (Lambda, S3, SQS, SFTP, Secrets Manager) and Terraform.
• Data engineering: ELT pipelines, data modeling, data quality checks, dashboards.
• Building LLM agents with tool use, MCP servers, or the Anthropic API.Software and Data Engineer | Page 2
• Microsoft SQL Server, Microsoft Graph, or background job frameworks (for example Inngest).

Work arrangement
No