Data Management Specialist

HTC Global Services; Hyderabad; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

HTC Global Services is seeking a data engineer specialist to manage incidents and changes, lead source-to-target mappings, and ensure scalable data pipelines on AWS. You will work with AI engineers and solution architects to optimize data onboarding and governance.

You will write detailed JIRA stories, assess data source impacts, and cooperate with infrastructure teams to maintain data quality and reliable platform performance.

Qualifications

• Experience with incident and change management workflows.
• Translate data requirements into source-to-target mappings with transformation logic.
• Collaborate with AI engineers, data modelers and solution architects.
• Hands-on ETL patterns on AWS.
• Write clear JIRA stories with business value and acceptance criteria.
• Assess impact of new data sources and changes on downstream platforms.
• Coordinate with infrastructure, DB and other teams.

Responsibilities

• Triage and resolve production incidents within SLA.
• Gather data requirements and translate them into mapping documents.
• Collaborate to ensure data pipelines support analytics at scale.
• Design and review ETL architecture patterns on AWS.
• Create and maintain detailed tasks in JIRA/ServiceNow.
• Flag data quality issues at the source and remediate.

Skills

Incident management

Data governance

Data quality

SQL

ETL design

AWS data services

JIRA/ServiceNow

Agile delivery

Tools

AWS Glue

Step Functions

S3

Redshift

Athena

JIRA

ServiceNow

SQL

Job description

• 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:
• 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 partitioning 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.
• Leverage AI-assisted tooling (code generation, automated testing, intelligent data profiling) as a work efficiencies multiplier.
• Hands-on SQL.
• Strong ETL architecture knowledge with practical AWS experience (Glue, Lambda, S3, IAM, CloudWatch)