Senior Backend Engineer - Financial Data Systems

Rogo; New York (NY); On-site; USD 150,000 - 230,000; Full time

Job summary

Rogo, a fast-growing enterprise AI company in New York, is seeking a Senior Backend Engineer on the Financial Data team. You will own data infrastructure that ingests, transforms, and serves financial datasets end-to-end, powering Rogo's AI models with timely and reliable data.

You will design scalable pipelines, optimize performance, and work across verticals to integrate market data vendors, proprietary sources, and third-party providers.

Qualifications

• Bachelor's in Computer Science or related field.
• 6+ years of professional software engineering experience, with a proven track record building complex, data‑intensive backend systems.
• 4+ years of hands‑on experience with GCP or equivalent cloud stack, building and scaling production‑grade services.
• Deep expertise in Python for backend and API systems; proficiency in at least one strongly typed language (Rust, C++, or Java), with Java being a strong plus.
• Mastery in designing large‑scale distributed systems — asynchronous patterns, streaming, queuing, caching strategies, and robust observability (logging, metrics, tracing).
• Strong command of relational databases and SQL — schema design, query optimization, indexing — alongside NoSQL, in‑memory (Redis), and columnar stores (BigQuery, Snowflake, Redshift).
• Proven experience building and operating large‑scale data pipelines — both streaming and batch — integrating heterogeneous financial data sources with high reliability and throughput.
• 3+ years of experience working closely with globally distributed engineering teams.
• Exceptional communication skills — able to articulate complex technical concepts to both technical and non‑technical stakeholders, and to influence engineering direction across teams without direct authority.
• Track record of shipping high‑quality products in unstructured, fast‑moving environments with full autonomy from conception to deployment.
• Demonstrated passion for corporate finance, capital markets, and financial data — you understand the standards, workflows, and zero‑tolerance for data quality issues that investment professionals demand.

Responsibilities

• Own critical data infrastructure: Take complex requirements and turn them into robust, scalable solutions that ingest, transform, and serve financial datasets reliably — end to end, from conception to production.
• Architect for scale and performance: Design and build highly scalable, cost-effective data processing pipelines that handle millions of documents and datapoints daily with very low latency and maximum throughput.
• Drive data integration: Build systems that connect heterogeneous financial datasets — market data vendors, proprietary sources, third‑party providers — into coherent, reliable, queryable platforms that power Rogo's AI layer.
• Enable AI with great data: Work closely with the Core AI team to ensure financial data is structured, clean, and served in ways that maximize the performance and reliability of Rogo's models and retrieval systems. Critically, the data pipelines, transformations, and interfaces you build are themselves powered by the same AI techniques and innovation used across the rest of the platform — you are not just feeding the machine, you are part of it.
• Handle customer data with care: Build secure, compliant integrations that treat enterprise customer data with the highest standards of trust, privacy, and reliability — from ingestion through to serving.

Skills

Python

Java

Rust

C++

Distributed systems

Data pipelines

SQL

NoSQL

Observability

Education

Bachelor's in Computer Science

Tools

GCP

BigQuery

Snowflake

Redshift

Redis

Job description

Rogo, a fast-growing enterprise AI company in New York, is seeking a Senior Backend Engineer on the Financial Data team. You will own data infrastructure that ingests, transforms, and serves financial datasets end-to-end, powering Rogo's AI models with timely and reliable data.

You will design scalable pipelines, optimize performance, and work across verticals to integrate market data vendors, proprietary sources, and third-party providers.