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

1. Integration & Data Movement

• Design and implement data movement across systems using:

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• 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

1. Data Preparation for GenAI

• Support data ingestion and preparation for GenAI use cases:

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• 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)

1. Delivery & Coordination

• Work across multiple teams:

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• 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