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 frameworksTransform and validate incoming data to meet customer requirements and SteelEye's internal data models/schemasDevelop in Python, using common data libraries (e.g., Pandas/Numpy) where appropriateImplement and evolve Pydantic schemas and data mappings, ensuring consistency and testabilityOrchestrate 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 documentationWork cross-functionally with Product and other engineering teams to clarify requirements and resolve data issuesTroubleshoot ingestion issues and data quality problems; propose small, practical improvementsUse Jira/Confluence to manage work transparently and communicate progress and blockers earlyRequirements2–3+ years of relevant experience in a Python engineering / data engineering roleStrong 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 teamsInterview 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 ReviewIntro call with Human Resources Business PartnerFirst Stage Overview Interview with our senior Data EngineersFinal Interview with our Head of Data EngineeringAbout SteelEye SteelEye is a dynamic B2B FinTech company dedicated to enabling financial institutions, including banks, investment firms, brokers, hedge funds, and asset managers, to efficiently and accurately meet their regulatory obligations under various global financial regulations. As the finance industry's pioneering integrated trade and communications surveillance solution, SteelEye empowers financial firms with data-driven tools and comprehensive insights, all from a single platform, allowing them to focus on what truly matters.At SteelEye, we pride ourselves on fostering a diverse, equitable, and inclusive workplace where everyone's contributions are valued. We are committed to being an inclusive employer, embracing individuals of all races, religions, gender identities, sexual orientations, national origins, ages, socioeconomic statuses, medical conditions or disabilities, and other protected statuses. We actively seek talent from diverse backgrounds, experiences, personalities, and perspectives, believing that our differences drive innovation and success.#J-18808-Ljbffr