Data & Integration Engineer, AI (Banking, 1-year renewable contract)

Dear Applicant,

If you or someone you know is interested, please send the CV directly to [HIDDEN TEXT] (most preferred, as I may overlook some CVs due to the high volume).

Please note that visa sponsorship is not available at this time.

Key Responsibilities

. Analyse business and technical requirements and translate them into end-to-end data flows, integration designs, interface specifications, and implementation plans.

. Collaborate with upstream and downstream teams to define data contracts, interfaces, and integration requirements.

. Identify gaps, inefficiencies, and risks in existing data movement processes, proposing practical solutions that balance delivery speed, quality, and maintainability.

. Design and implement data integrations across enterprise systems using REST APIs, SFTP, file-based transfers, and batch processing.

. Develop scripts and programs to retrieve, process, transform, and transfer data across different integration systems.

. Develop and maintain APIs to enable seamless integration between applications, data platforms, and downstream systems.

. Coordinate data integrations across enterprise data platforms, including Informatica, Cloudera, and similar technologies.

. Ensure data is accurately mapped, transformed, aggregated, reconciled, and delivered to target systems in the required format.

. Support data ingestion and preparation for Generative AI (GenAI) use cases, including document ingestion, data enrichment, aggregation, and transformation.

. Work with structured and unstructured data to ensure it is suitable for downstream AI workflows, including Retrieval-Augmented Generation (RAG), search, and investigation use cases.

. Collaborate with data platform, application, infrastructure, and security teams to deliver reliable end-to-end integrations.

. Troubleshoot integration and data pipeline issues across different environments, ensuring appropriate error handling, monitoring, and recovery mechanisms.

. Support System Integration Testing (SIT), User Acceptance Testing (UAT), and production deployments.

. Maintain clear documentation of data flows, mappings, interfaces, and integration processes.

Key Requirements

. Min 4 years of experience in system analysis, integration engineering, data engineering, or technical delivery roles.

. Strong ability to translate business and technical requirements into system flows, data flows, interface specifications, and implementation plans.

. Experience delivering enterprise integrations and coordinating with upstream and downstream teams.

. Hands-on experience with REST APIs, SFTP, batch processing, file-based integrations, and data pipeline orchestration.

. Good understanding of data mapping, transformation, aggregation, reconciliation, and data quality controls.

. Strong SQL skills and basic to intermediate Python programming skills for data processing, scripting, automation, and troubleshooting.

. Exposure to Java programming.

. Experience with enterprise data platforms such as Informatica, Cloudera, or equivalent technologies. Working experience with Informatica is preferred.

. Working knowledge of Git, branching strategies, pull requests, code reviews, and controlled release practices.

. Familiarity with CI/CD pipelines, Jira, Confluence, and enterprise deployment processes.

. Experience with Control-M or equivalent job scheduling tools.

. Familiarity with logging and monitoring tools, such as OpenTelemetry (OTel), Splunk, or Elastic Stack.

. Exposure to GenAI concepts, including document ingestion, RAG, embeddings, and data preparation for AI workflows.

. Strong analytical and problem-solving skills, with the ability to challenge ineffective designs and recommend practical alternatives.

. Excellent communication and stakeholder management skills, with the ability to coordinate across business, application, data, infrastructure, and security teams.

. A strong understanding of end-to-end system flows and the ability to troubleshoot and implement solutions in complex enterprise environments.