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 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
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