Data Management Specialist

• Incident management and change management.
• Source-to-target mapping and data onboarding.
• Data quality management and impact assessment.
• Agile delivery, documentation, and platform enablement.

Scope of Services / Specifications:

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

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.

1. 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.

1. 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.

1. 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.

1. Hand off refined requirements to scrum teams and remain partitioning during development and testing.

1. Identify and flag data quality issues at source, working with data stewards and source owners to remediate before data enters the integration layer.
2. Leverage AI-assisted tooling (code generation, automated testing, intelligent data profiling) as a work efficiencies multiplier.
3. Hands-on SQL.
4. Strong ETL architecture knowledge with practical AWS experience (Glue, Lambda, S3, IAM, CloudWatch)