Lead 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 Data Lake 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)

4. Delivery & Coordination

Requirements

Below are the key skillsets that will be required for all relevant tasks mentioned:

· 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.

Key Domain/ Technical Skills:

· Data Engineering,

· System Integrations,

· Python, SQL