Data & Integration Engineer

Responsibilities 1. System Analysis & Design • Analyse business/technical requirements and translate them into data flows and integration designs. • Work with upstream and downstream teams to define data contracts and interfaces. • Identify gaps, inefficiencies and risks in current data movement processes. • Propose pragmatic solutions balancing speed, quality and maintainability. 2. Integration & Data Movement • Design and implement data movement across systems using: APIs SFTP and file based transfers Batch pipelines. • Coordinate integrations across systems in the DataLake ecosystem (Informatica, Cloudera, etc.) \ • Ensure data is correctly transformed, mapped and delivered to target systems. • Troubleshoot integration issues across environments. 3. Data Preparation for GenAI • Support data ingestion and preparation for GenAI use cases: document ingestion data aggregation enrichment and transformation • Work with structured and unstructured data • Ensure data is usable for downstream AI workflows (RAG, search, investigation flows) You are not asking them to build models, just make data usable for them. 4. Delivery & Coordination • Work across multiple teams: data platforms application teams infrastructure security • Support SIT, UAT and production rollouts. • Ensure integration reliability, error handling and monitoring. • Document flows, mappings and interfaces clearly. Requirement: • 5-10 years of experience in system analysis, integration engineering, data engineering or technical delivery roles. • Strong ability to translate requirements into system flows, data flows, interface specifications and implementation plans. • Experience working with upstream and downstream teams to define and deliver enterprise integrations. • Practical experience with REST APIs, SFTP, batch processing, file based integration and data pipeline orchestration. • Good understanding of data mapping, transformation, aggregation, reconciliation and data quality controls. • Good SQL skills and basic to moderate Python skills for data handling, scripting, automation and troubleshooting. • Exposure to Java • Exposure to Informatica, Cloudera or similar enterprise data platforms. • Working knowledge of Git, branching, pull requests, code reviews and controlled release practices. • Familiarity with CI/CD, Jira, Confluence and enterprise deployment processes. • Experience with Control M or equivalent scheduling tools. • Familiarity with logging (OTEL) and monitoring tools such as Splunk Elastic Stack. • Exposure to GenAI concepts such as document ingestion, RAG, embeddings and data preparation for AI workflows.