Data Engineer

SteelEye is a fast moving RegTech (Regulatory Technology) start-up that is helping financial companies (e.g. banks, investment firms, brokers, hedge funds, and asset managers) meet their obligations under various global financial regulations.

Our work enhances financial compliance, prevents market abuse, and promotes trust in the financial markets. Our people are passionate about leveraging data and technology to make this happen.

As a Data Integration Engineer, you’ll build and maintain Comms and Trades data ETLs—transforming raw client data into validated, searchable datasets that power the SteelEye platform. You’ll deliver scoped integration work with regular guidance, collaborating closely with Engineering, Product, and internal integration stakeholders to ensure data arrives reliably and meets our schemas and quality standards.

Key responsibilities:

- Build and maintain ETL/ET+L pipelines for communications and trades datasets, following established patterns and frameworks.

- Transform and validate incoming data to meet customer requirements and SteelEye’s internal data models/schemas.

- Develop in Python, using common data libraries (e.g., Pandas/Numpy) where appropriate.

- Implement and evolve Pydantic schemas and data mappings, ensuring consistency and testability.

- Orchestrate and schedule workflows using Conductor and Kubernetes. Experience with other orchestrating frameworks such as Prefect, Temporal or Airflow is also valued.

- Contribute to code reviews, improve code quality, and write clear technical documentation.

- Work cross-functionally with Product and other engineering teams to clarify requirements and resolve data issues.

- Troubleshoot ingestion issues and data quality problems; propose small, practical improvements.

- Use Jira/Confluence to manage work transparently and communicate progress and blockers early.

Requirements

- 2–3+ years of relevant experience in a Python engineering / data engineering role.

- Strong Python fundamentals (clean, testable code; debugging; working with structured/unstructured data).

- Comfortable working with schemas/validation (Pydantic ideal).

- Familiarity with cloud and container tooling (AWS, Docker, Kubernetes).

- Strong written and verbal communication; able to collaborate effectively across teams.

Interview Process: The interview process is structured to assess candidates thoroughly across various competencies and skills relevant to the role. Here's a description of each stage:

- CV Review

- Intro call with Human Resources Business Partner

- First Stage Overview Interview with our Data Engineers

- Final Interview with our Head of Data Engineering

Apply directly via:

https://careers.steel-eye.com/jobs/7278917-data-engineer

Contacts and Address

Linkedin

Comply with confidence. SteelEye is the first and only truly integrated trade and communications surveillance solution. We empower you with the data-driven tools and complete insights you need to focus on what matters, all from a single platform.