Data & Integration Engineer
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Role: Data & Integration Engineer
Key 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
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