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- Incident management and change management.
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- Source-to-target mapping and data onboarding.
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- Data quality management and impact assessment.
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- Agile delivery, documentation, and platform enablement.
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nScope of Services / Specifications:

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- Triage and resolution of production incidents within SLA
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- 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.
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- 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.
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- 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.
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- 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.
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- 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.
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- Hand off refined requirements to scrum teams and remain engaged during development and testing.
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- Identify and flag data quality issues at source, working with data stewards and source owners to remediate before data enters the integration layer.
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- Support data onboarding, lineage documentation, operational readiness, and the adoption of AI-assisted tools to improve delivery efficiency and data platform effectiveness.
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- Triage and resolution of production incidents within SLA
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- Daily monitoring of batch cycles, interfaces, and data loads
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- Reconciliation support (positions, transactions, pricing, accounting)
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- User access and entitlement support
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- Data validation and correction
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- Coordination with infrastructure, DB, and upstream/downstream systems/teams
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- Minor enhancements and configuration updates
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- Ticket management (ServiceNow/Jira) and stakeholder communication
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- On-Call / After-Hours Escalation:
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- 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.
n
- 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.
n

nDesign 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.

n

n
- 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.
n
- 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.
n
- Hand off refined requirements to scrum teams and remain partitioning during development and testing.
n
- Identify and flag data quality issues at source, working with data stewards and source owners to remediate before data enters the integration layer.
n
- Leverage AI-assisted tooling (code generation, automated testing, intelligent data profiling) as a work efficiencies multiplier.
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- Hands-on SQL.
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- Robust ETL architecture knowledge with practical AWS experience (Glue, Lambda, S3, IAM, CloudWatch)
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📌 Data Management Specialist (Hyderabad)
🏢 HTC Global Services
📍 Hyderabad