Data &

- Analytics Data Pipelines Lead 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: o Source systems and external data providers o Landing/staging databases o Enterprise data warehouses o Data lake environments o Analytics platforms o 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.
- Partner with business intelligence, analytics, reporting, application, and infrastructure teams.
- 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.
- Solid understanding of data warehousing, dimensional modeling, and data lake architectures.
- Experience implementing data quality and data governance practices.
- Strong 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: o Azure Data Factory o Databricks o Synapse Analytics o Snowflake o Informatica o Talend o SSIS o Kafka o Fivetran o 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 Architectures • Data Quality Management • Metadata Management • Data Lineage • Production Support Operations • Monitoring and Observability • ServiceNow Administration and Workflow Processes Leadership Competencies • Operational Excellence • Stakeholder Management • Team Leadership • Incident Management • Strategic Planning • Continuous Improvement • Risk Management • Communication and Influence Experience Level Senior Level

📌 Data Pipelines Lead - Data & Analytics (Bengaluru)
🏢 NTT DATA Global Delivery Services
📍 Bengaluru