Key Responsibilities Data Pipeline Leadership - Lead the design, development, deployment, and support of enterprise data pipelines and integration solutions. - Establish standards, patterns, and best practices for data ingestion, transformation, orchestration, and delivery. - Oversee data movement across the full analytics lifecycle: - Source systems and external data providers - Landing/staging databases - Enterprise data warehouses - Data lake environments - Analytics platforms - Reporting databases and data marts - Ensure scalable, secure, and high-performing data integration architectures. - Drive automation and operational efficiency within data pipeline environments. Data Integration Engineering - Manage batch, near-real-time, and streaming data ingestion processes. - Coordinate data onboarding and integration activities for new source systems and external data suppliers. - Define and maintain ETL, ELT, and data replication standards. - Support cloud and on-premises data integration platforms. - Collaborate with enterprise architects to align data movement solutions with strategic architecture standards. Data Quality Governance - Establish and maintain enterprise data quality controls and monitoring processes. - Define data validation, reconciliation, exception handling, and alerting frameworks. - Monitor pipeline performance and proactively identify data integrity issues. - Partner with data governance teams to ensure compliance with organizational standards. - Implement and maintain data lineage documentation across data movement processes. - Support audit, compliance, and regulatory reporting requirements related to data traceability. Data Lineage Metadata Management - Document end-to-end data flows across all pipeline stages. - Maintain lineage mapping between source systems, transformation processes,



and downstream reporting assets. - Ensure metadata accuracy and availability for analytical consumers. - Support impact assessments related to upstream and downstream changes. - Drive adoption of data catalog and metadata management capabilities. Production Support Operations - Lead operational support for production data integration and analytics pipelines. - Manage ServiceNow incidents, service requests, problem records, and change activities related to data pipeline operations. - Coordinate incident triage, root cause analysis, issue resolution, and stakeholder communications. - Establish and monitor service level agreements (SLAs) and operational metrics. - Ensure rapid resolution of critical data availability and quality issues. - Coordinate production releases and change management activities. Monitoring Reliability - Implement pipeline monitoring, observability, and alerting solutions. - Track pipeline health, throughput, latency, failure rates, and data quality metrics. - Develop operational dashboards and reporting for platform performance. - Lead efforts to improve platform reliability, resiliency, and recoverability. - Support disaster recovery and business continuity processes for critical data assets. Stakeholder Team Leadership - Serve as the primary point of contact for data pipeline operations and support. - Mentor data engineers and analysts on integration standards and best practices.



- Facilitate prioritization of enhancements, technical debt reduction, and operational improvements. - Communicate risks, issues, and performance metrics to leadership. - ____________________ Required Qualifications - Bachelor's degree in Computer Science, Information Systems, Engineering, Data Analytics, or related field. - 7+ years of experience in data engineering, data integration, ETL/ELT development, or data platform operations. - 3+ years of experience leading enterprise-scale data pipeline and integration initiatives. - Experience supporting production data environments and operational processes. - Experience managing incident, problem, and change management processes within ServiceNow or similar ITSM platforms. - Strong understanding of data warehousing, dimensional modeling, and data lake architectures. - Experience implementing data quality and data governance practices. - Robust analytical, troubleshooting, and problem-solving skills. - ____________________ Preferred Qualifications - Experience with cloud data platforms such as Oracle, Azure, AWS, or Google Cloud. - Experience with the Teradata platform and Teradata Data Mover (TDM) - Experience with modern data engineering platforms including: - Azure Data Factory - Databricks - Synapse Analytics - Snowflake - Informatica - Talend - SSIS - Kafka - Fivetran - dbt - Experience with metadata management and data lineage tools. - Knowledge of DevOps, CI/CD, infrastructure automation, and data observability platforms. - Familiarity with Agile delivery methodologies and product operating models. - ____________________ Key Competencies Technical Competencies - Data Engineering - ETL/ELT Architecture - Data Warehousing - Data Lake Arch

📌 Data & Analytics Data Pipelines Lead (Bengaluru)
🏢 NTT
📍 Bengaluru