Lead Data & Integration Engineer - Part-Time

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

• 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

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