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.