Associate to mid-level data engineer

About the role

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DiversyFund is hiring an associate to mid-level data engineer to own the data behind a real estate investment platform and the AI products built on top of it. You'll be reporting directly to VP of Engineering and AI Product Development.

We run AI agents across our business: investor services, CRM, outbound email and SMS campaigns, lead research and internal operations. They are only as good as the data they read and write. Your job is to make that data clean, organized, trustworthy and secure, so our people and our agents can rely on it.

This role suits someone early in their career who wants to prove themselves, or an experienced engineer who wants to start fast. You'll get hands-on mentoring, real ownership from week one, and a clear path to grow.

Term: 13-week contract, with the opportunity to extend or convert to full time

Rate: $30 to $40 an hour, based on experience

Hours: Full time, 40 hours a week

Start: November 9, 2026

Location: San Diego, CA On-site

Reports to: VP of Engineering and AI Product Development

What you'll do:

• Own our core databases. Three PostgreSQL systems of record hold investor capital, CRM and lead data, plus several smaller app databases. Keep their schemas documented, consistent and owned by one codebase each.
• Make schema changes safe. Manage migrations with version control and review, so no change can silently break another app or lose data.
• Build and run data pipelines. Ingest, clean, deduplicate and enrich data from public records, CRM exports, email and SMS platforms and payment systems. Move pipelines off local machines onto scheduled, monitored cloud jobs.
• Make event delivery reliable. Add retries, dead-letter handling and replay to the event flows between our investor portal and CRM.
• Prepare data for AI agents. Shape the data our AI assistants and campaign agents read and write, and help build the evaluation datasets that check their answers.
• Build data-quality checks. Validation, reconciliation and alerts that catch bad records before they reach investors, campaigns or reports.
• Protect sensitive data. Classify and mask personal and financial data, keep it out of code repositories, and give non-production work safe synthetic or masked copies.
• Support compliance. Track data lineage, retention and access, and produce evidence for SOC 2 and our Technology Operations team.
• Power reporting. Build the models behind our dashboards and business reports.
• Review agent-written code. Much of our code is written with AI coding tools. You'll review and test what they produce in your area before it ships.

What you need

• 1 to 4 years of data or software engineering experience, including internships, contract work or substantial projects.
• SQL: strong PostgreSQL: schema design, joins, window functions, indexes, query plans and migrations.
• NoSQL: hands-on with at least one document or key-value store, such as DynamoDB, MongoDB or Redis, and good judgment about when to use JSON columns in Postgres instead.
• AI and agentic systems: you've built with LLM APIs or agent frameworks, such as tool calling, retrieval or MCP servers, and understand how data quality affects model output.
• Cloud and AWS: working knowledge of S3, IAM and at least one of Lambda, App Runner, RDS or SES; comfortable with serverless deployment.
• Programming: TypeScript or Python. Ideally both, since our apps are TypeScript and our pipelines mix in Python.
• Pipelines: experience with ETL or ELT, scheduled jobs and idempotent, retry processing.
• Big data, data lakes, data refineries
• Data handling: you treat personal and financial data with care: least privilege, masking, no secrets or customer data in git.
• Git and code review: comfortable with pull requests, CI and writing tests for data code.
• AI-assisted development: you use tools like Claude Code or Cursor daily, and you catch their mistakes.
• Communication: you explain a data problem clearly to non-engineers and write good documentation.

Nice to have

• Fintech experience payments, ACH, KYC or AML, ledgers, investor reporting, or work under SEC or FINRA rules.
• Real estate, lending or investment platform data.
• Drizzle, Prisma or another TypeScript ORM; Neon or serverless Postgres.
• Vercel, Next.js, or job runners such as Trigger.dev.
• DuckDB, dbt or similar analytics tools.
• Vector databases or embeddings, such as pgvector.
• Event-driven systems: queues, webhooks, outbox patterns.
• Consent and opt-out data for email and SMS, such as CAN-SPAM and TCPA.
• SOC 2 evidence or data governance work.
• Public data sources, such as SEC, Census or other government datasets.

How we hire

We aim to go from first conversation to offer very quickly.

1. 20-minute screen with the VP of Engineering.
2. Interview/assessment on a sanitized, synthetic dataset. You'll design a schema change and its migration, write a cleaning and deduplication pipeline, and review an AI agent's SQL for mistakes.
3. Review conversation walking through your assessment and past projects.
4. References check, then an offer.

We hire for potential and drive. xhyhwjd If you meet most of the requirements and are hungry to get building and learn the rest, apply immediately.