Senior Backend Engineer

About Daloopa, Inc.

The data layer behind how AI actually works in finance. Daloopa turns messy financial disclosures into audit-grade, model-ready data used by institutions like Morgan Stanley. Its infrastructure sits behind flagship launches such as Anthropic's Claude for Financial Services and OpenAI's MCP-based financial workflows via Daloopa's official ChatGPT connector, giving AI agents direct access to verified, traceable fundamentals.

It doesn't just move data; it makes it usable for LLMs in high-stakes, regulated environments where accuracy, sourcing, and auditability actually matter. This role in production AI for finance has been recognized by outlets like Fast Company 's Most Innovative Companies 2026 list.

The Role:

Daloopa's mission is to become the market leader in high-quality, actionable data for the world's top investment professionals. We're judged on three things: coverage, speed, and accuracy. Behind those three numbers is a large operations team of financial analysts who build, update, and review models for thousands of public companies, and we're scaling that coverage toward 10,000 tickers.

At that scale, the way the operations floor runs becomes a product in its own right. Today a lot of that work lives in Excel trackers, Slack threads, and manual hand-offs: deciding which companies report when, who picks up which model, how reviews get done, and where an analyst goes to find the right process. That doesn't scale, and it's where this role comes in.

As a Senior Backend Engineer on Operations Transformation, you'll sit in the Engineering organization and partner directly with our Operations and Transformation leaders, who own the product requirements. You'll build the services behind a new generation of internal products: AI-assisted automation of analyst work, workflow systems that replace spreadsheets and Slack back-and-forth, and the planning and visibility data team leads and sector heads need to run a 10,000-ticker operation. You'll own the backend end to end (APIs, data model, background jobs, and LLM pipelines), work with the engineers building the interfaces analysts use, and integrate with Daloopa's core content platforms, following the same engineering standards as the rest of Engineering.

A large part of the role is applied AI. That means getting close to LLMs: building pipelines, writing evals, and iterating on prompting strategies and model outputs until they are reliable enough for analysts to trust. In a domain where the quality of the output moves capital, "it looked right in the demo" isn't the bar.

If you like being close to your users, building backend systems people rely on every day, and applying AI where it measurably saves time rather than where it looks impressive, this is the role.

What Sets You Up for Success:

• 5+ years of professional backend engineering experience.
• Strong expertise in Python and Django (or an equivalent backend framework); FastAPI experience is a plus.
• Deep understanding of relational and non-relational databases (MySQL, PostgreSQL, Redis), including schema design and query performance under real load.
• Solid experience with distributed systems, caching, and asynchronous task queues (Celery or equivalent).
• Solid understanding of RESTful and/or GraphQL API design.
• Hands-on experience working with LLMs in production: building pipelines, writing evals, iterating on prompts and model outputs, using structured outputs, and handling failures, latency, and cost.
• Comfortable designing for observability, resilience, and graceful degradation in systems people depend on hour to hour.
• Experience building internal tools, workflow systems, or automation that replaced manual or spreadsheet-driven processes.
• Comfortable working directly with non-engineering stakeholders: turning loosely defined operational problems into scoped, shippable increments.
• Track record of owning backend projects end to end with minimal hand-holding.

Bonus Points for:

• Experience with workflow, scheduling, or resource-planning systems (assignment, capacity, SLAs, status tracking).
• Experience building retrieval-augmented (RAG) assistants or knowledge bases over internal documentation.
• Experience with LLM observability and evaluation tooling (tracing, eval datasets, regression testing of prompts).
• Experience automating Excel/Google Sheets workflows or migrating them into proper applications.
• Background in fintech, financial data, or other domains where data quality is mission-critical and audit trails matter.
• Familiarity with financial fundamentals: 10-Ks, 10-Qs, earnings releases, transcripts, segment reporting.
• Prior experience at growth-stage startups, working in fast-moving teams with changing priorities.