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Data Integration Engineer CIL Modernization
Job Location:
Monthly Salary:
Posted:
24 October 2026 (Yesterday)
Posted:
Application Deadline:
21 January 2027
Application Deadline:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
Title: Data Integration Engineer -CIL Modernization
Location: Bangalore Hyderabad Chennai
Exp: 8 Years
Job Description:
Mandatory Skill
AI Data Engineer with experience in core ETL execution including data cleansing standardization entity resolution and data conformance across multiple source systems. Skilled in building and optimizing scalable data pipelines for large-scale data integration projects.
Hands-on SQL.
Strong ETL architecture knowledge with practical AWS experience (Glue Lambda S3 IAM CloudWatch)
Collaborate directly with AI engineers data modelers and solution architects to ensure data pipelines serve both traditional analytics and support transforming data with high volumes capability.
Design and review ETL architecture patterns on AWS (Glue Step Functions S3 Redshift/Athena) providing hands-on guidance on job orchestration partitioning strategies and historic storages.
1. Partner with source system owners and business stakeholders to gather data requirements and translate them into clear actionable source-to-target mapping documents with documented transformation logic and acceptance criteria.
2. Collaborate directly with AI engineers data modelers and solution architects to ensure data pipelines serve both traditional analytics and supports transforming data with high volumes capability.
Design and review ETL architecture patterns on AWS (Glue Step Functions S3 Redshift/Athena) providing hands-on guidance on job orchestration partitioning strategies and historic storages.
3. Write detailed JIRA stories covering business value mapping changes data onboarding steps and expected platform impact stories that engineering teams can pick up with minimum transition support.
4. Assess the impact of new data sources product changes or business enhancements on downstream screening and detection platforms proactively flagging risks before they hit production.
5. Hand off refined requirements to scrum teams and remain partitioning during development and testing.
6. Identify and flag data quality issues at source working with data stewards and source owners to remediate before data enters the integration layer.
7. Leverage AI-assisted tooling (code generation automated testing intelligent data profiling) as a work efficiencies multiplier.
Location: Bangalore Hyderabad Chennai
Exp: 8 Years
Job Description:
Mandatory Skill
AI Data Engineer with experience in core ETL execution including data cleansing standardization entity resolution and data conformance across multiple source systems. Skilled in building and optimizing scalable data pipelines for large-scale data integration projects.
Hands-on SQL.
Strong ETL architecture knowledge with practical AWS experience (Glue Lambda S3 IAM CloudWatch)
Collaborate directly with AI engineers data modelers and solution architects to ensure data pipelines serve both traditional analytics and support transforming data with high volumes capability.
Design and review ETL architecture patterns on AWS (Glue Step Functions S3 Redshift/Athena) providing hands-on guidance on job orchestration partitioning strategies and historic storages.
1. Partner with source system owners and business stakeholders to gather data requirements and translate them into clear actionable source-to-target mapping documents with documented transformation logic and acceptance criteria.
2. Collaborate directly with AI engineers data modelers and solution architects to ensure data pipelines serve both traditional analytics and supports transforming data with high volumes capability.
Design and review ETL architecture patterns on AWS (Glue Step Functions S3 Redshift/Athena) providing hands-on guidance on job orchestration partitioning strategies and historic storages.
3. Write detailed JIRA stories covering business value mapping changes data onboarding steps and expected platform impact stories that engineering teams can pick up with minimum transition support.
4. Assess the impact of new data sources product changes or business enhancements on downstream screening and detection platforms proactively flagging risks before they hit production.
5. Hand off refined requirements to scrum teams and remain partitioning during development and testing.
6. Identify and flag data quality issues at source working with data stewards and source owners to remediate before data enters the integration layer.
7. Leverage AI-assisted tooling (code generation automated testing intelligent data profiling) as a work efficiencies multiplier.
Required Experience:
IC
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