Senior Data Engineer

Rho is the modern banking platform built for the AI era. Startups and growth-stage companies can open accounts in minutes, issue cards, manage expenses, pay bills, and close the books – all in one connected platform backed by real human support.

Our team is looking for a Senior Data Engineer to join our data products team and help build & scale Rho's commercial banking technology.

The Data team builds data products for internal stakeholders and customers directly, maintaining a strong data driven culture across the company. This means treating data, pipelines, and ML systems as products in their own right: owned, versioned, documented, and built for other teams to discover and consume, not just infrastructure that sits behind the scenes. A key part of this role is building horizontal data products: reusable, ML-powered systems like an internal OCR pipeline, a transaction coding suggestion engine, and RAG-based and agentic systems that leverage our data to automate and augment internal workflows, that serve multiple teams and use cases across the company.

Technologies we use for data: Python, Go, Snowflake, DBT, PostgreSQL, Kubernetes, Terraform, Prometheus, Google Cloud Services, Omni, Hex

The ideal candidate is a strong software and data engineer with good taste & judgement, someone who knows what good looks like, and subscribes to a “strong opinions, weakly held” mindset.

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  Design, build, and own data products, reliable, scalable, well-documented pipelines and datasets that other teams can discover, trust, and build on directly

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  Apply strong software engineering practices (clean code, testing, version control, CI/CD) to data products and ML systems, treating them with the same rigor as production software, while moving fast in AI-first world

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  Build horizontal, ML-driven data products, such as our OCR pipeline for document processing, our transaction coding suggestion system, and RAG/agentic tools, designed to be reused across multiple teams rather than built for a single use case

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  Define and uphold data product quality: implement quality checks, monitoring, alerts, and SLAs so consumers of a data product can rely on it the way they'd rely on any well-run internal API

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  Partner directly with the stakeholders who consume your data products to understand their needs, gather feedback, and iterate, treating them as customers rather than downstream requesters

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  Collaborate with business intelligence and analytics teams to turn business needs into data products that are easy to find, understand, and self-serve from

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  Lead initiatives to grow Rho's catalog of data products and improve the platform that supports them, expanding what other teams can build on top of

• 5+ years of experience in data engineering, software engineering, or DevOps

• Proficiency in workflow orchestrators such as Airflow, Dagster, or Prefect

• Experience with major data platforms including Snowflake, Databricks, BigQuery, or in-house HDFS-based solutions

• Skilled at building data infrastructure using GCP, AWS, or comparable cloud providers

• Comfortable managing and deploying services on Kubernetes

• Practical experience with Terraform or Pulumi for automating infrastructure

• Advanced programming abilities in Python and Go are integral to this role, along with strong SQL skills (Java experience also considered)

• Proven experience building, owning, and iterating on data products or data pipelines based on consumer feedback, not just building infrastructure in isolation

• Experience designing systems for reuse across multiple teams

• Good communication skills and a team-oriented, collaborative approach, comfortable working directly with the people who consume what you build

• Experience building a data platform or data product suite from the ground up

• Experience integrating or deploying ML models into production systems (e.g., classification, suggestion, or extraction models)

• Experience with RAG (Retrieval-Augmented Generation) systems and vector databases

• Familiarity with ML infrastructure tooling such as MLflow

• Experience building or deploying agents

• Experience with data modeling and data product approaches such as Data Mesh, Kimball, Inmon, Data Vault, or similar

• Experience working with streaming data systems

• Familiarity with Prometheus or other time-series databases used for monitoring

• Experience with business intelligence (BI) tools such as Omni