Engineering Manager

Daloopa accelerates decision-making for investment professionals by transforming complex financial data into actionable intelligence through AI-powered infrastructure. Founded by Thomas Li, Daloopa automates the extraction, organization, and delivery of deeply sourced, audit-ready financial datasets, enabling clients to update models faster, reduce errors, and focus on higher-value analysis. With innovations like MCP for Financial Services, which connects verified data directly to AI agents and industry workflows, Daloopa is trusted by leading global institutions such as Morgan Stanley and supported by prominent investors. Their robust, scalable platform offers accurate, traceable, and seamlessly integrated data solutions, giving financial teams a decisive edge in research and compliance, redefining the future of fundamental data for buy-side and sell-side professionals worldwide.

Daloopa's engineering teams are rapidly expanding to keep up with our ambitious vision and growing business. We have a unique opportunity for an experienced Engineering Manager to join the Autotagger Team, the innovation hub where we harness speed and intelligence at scale. This team is the heart of Daloopa's data acquisition engine and plays a fundamental role in our ability to fuel the next generation of financial intelligence.

• Deep understanding of what excellence in engineering looks like, and a track record of hiring, developing, and retaining strong technical talent.

• Proven experience building high-performing, healthy engineering teams that value trust, accountability, and continuous improvement.

• Strong ability to set and uphold high standards for technical execution, code quality, and operational readiness in production systems.

• Demonstrated success defining and managing a team’s roadmap in partnership with cross-functional stakeholders, making clear trade offs between scope, quality, and timelines.

• Hands-on technical leadership background (e.g., backend, data, or infrastructure), with the ability to engage deeply in design and code reviews when needed.

• Experience with large-scale data processing, distributed systems, or ML/LLM-driven products, and the engineering practices required to run them reliably in production.

• History of leading initiatives that improve how multiple teams work, such as incident review processes, standards, tooling, or knowledge sharing.

• Strong communication and collaboration skills, with the ability to build trusted relationships across Engineering, Product, and business teams.

• Experience with financial data products or pipelines (e.g., fundamental data, market data, research or analytics platforms).

• Background working with hybrid systems that mix deterministic rules, heuristics, and AI/LLM-based components.

• Exposure to data quality engineering, data lineage, or auditability in domains where accuracy and traceability are critical. Just give me one second please

• Familiarity with modern data and workflow orchestration tools, and with running distributed systems in cloud environments.

• Experience partnering closely with client-facing teams or directly influencing workflows for analysts, portfolio managers, or similar end-users.

• Prior work in environments with regulatory, compliance, or strict data governance requirements.