Delivery Manager – Data & Analytics

GSPANN Technologies, Inc.; Hyderabad; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

GSPANN Technologies, Inc. is seeking a Delivery Manager with deep expertise in Data & Analytics to lead Managed Services for enterprise data platforms and analytics workloads.

The role focuses on Azure data ecosystem technologies, including Azure Data Factory, Azure Databricks, Microsoft Fabric, and PySpark, ensuring SLA-driven operations and service governance. The role requires leading a team of 12+ Data Engineers, Support Analysts, and Operations Specialists, mentoring staff and aligning

Qualifications

• 12+ years of overall experience, including 4–6 years in Data and Analytics delivery.
• Experience leading Managed Services engagements (preferred).
• Strong ITIL knowledge across incident, problem and change management.
• Lead and manage teams of 12+ professionals across engineering and support roles.
• Hands-on expertise in Data Analytics and Data Engineering.
• Expertise in the Microsoft Azure Data ecosystem (ADF, Databricks, Fabric, PySpark).
• Solid understanding of data architecture, ETL/ELT pipelines, data modeling, production data systems.

Responsibilities

• Lead end-to-end delivery of Data and Analytics services under the Managed Services model.
• Manage BAU support, enhancements, and operational activities across data pipelines and analytics workloads.
• Oversee L1–L3 support, ensuring SLA compliance and timely incident resolution.
• Monitor production environments and drive automation and performance improvements.
• Provide technical leadership across the Azure Data ecosystem and PySpark.
• Guide ETL/ELT frameworks, data quality checks, monitoring, and governance.
• Collaborate with DevOps to design and provision infrastructure for Managed Services.
• Support CI/CD pipelines, monitoring dashboards, and automation initiatives to improve efficiency.
• Ensure compliance with enterprise DevOps standards and governance.
• Lead governance through KPI tracking, SLA reporting, and capacity planning.
• Present governance dashboards and metrics to leadership and clients.
• Lead a team of 12+ and ensure balanced workloads and coverage.

Skills

Azure Data Factory

Azure Databricks

Microsoft Fabric

PySpark

ITIL

Team Leadership

SLA Management

Data Engineering

ETL/ELT

DevOps Collaboration

Tools

Python

Apache Spark

CI/CD pipelines

Monitoring Dashboards

Job description

Description

GSPANN is hiring a Delivery Manager with expertise in Data & Analytics to lead Managed Services delivery for enterprise data platforms and analytics workloads. The role focuses on Azure data ecosystem technologies, including Azure Data Factory, Azure Databricks, Microsoft Fabric, and PySpark, while ensuring SLA-driven operations and service governance.

Roles and Responsibilities

• Lead and drive end-to-end delivery of Data and Analytics services under the Managed Services operating model.
• Manage Business-As-Usual (BAU) support, enhancements, and operational activities across enterprise data pipelines and analytics workloads.
• Oversee Level 1 to Level 3 (L1–L3) support operations, ensuring Service Level Agreement (SLA) compliance, timely incident resolution, problem management, and uninterrupted service delivery.
• Monitor the stability of production environments and implement proactive improvements, automation opportunities, and performance optimizations.
• Apply strong hands-on expertise in Data and Analytics engineering to guide architectural decisions and operational improvements.
• Provide technical leadership across the Microsoft Azure Data ecosystem, including ADF, Azure Databricks, Microsoft Fabric, and Python-based Apache Spark (PySpark).
• Guide teams on Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) frameworks, data engineering best practices, data quality checks, monitoring strategies, and platform governance.
• Troubleshoot production issues, perform Root-Cause Analysis (RCA), and drive corrective and preventive action plans.
• Collaborate with DevOps teams to design, provision, and optimize infrastructure required for Managed Services delivery.
• Support Continuous Integration and Continuous Deployment (CI/CD) pipelines, environment provisioning, monitoring dashboards, and automation initiatives to improve service efficiency.
• Ensure compliance with enterprise DevOps standards, security policies, and operational governance frameworks.
• Drive Managed Services governance through service review meetings, Key Performance Indicator (KPI) tracking, SLA reporting, ticket trend analysis, capacity planning, and resource utilization monitoring.
• Generate and present governance dashboards, service reports, and performance metrics to internal leadership and client stakeholders.
• Lead and manage a team of 12+ Data Engineers, Support Analysts, and Operations Specialists, ensuring high-quality service delivery.
• Allocate tasks, balance workloads, and maintain operational coverage across shifts when required.
• Mentor team members, conduct performance evaluations, and strengthen capability development within the Data and Analytics Managed Services practice.
• Act as the primary point of contact for client engagement within the Managed Services framework.
• Align service delivery with client priorities and ensure high levels of customer satisfaction.
• Manage escalations, negotiate delivery priorities, and maintain strong collaboration with business and technology stakeholders.

Skills and Experience

• 12+ years of overall experience, including 4–6 years in Data and Analytics delivery, with prior experience leading Managed Services engagements (preferred).
• Demonstrate strong knowledge of Information Technology Infrastructure Library (ITIL) processes, including incident management, problem management, and change management.
• Lead and manage teams of 12+ technical professionals across engineering and support roles.
• Possess strong hands-on expertise in Data Analytics and Data Engineering environments.
• Apply deep expertise in the Microsoft Azure Data ecosystem, including ADF, Azure Databricks, Microsoft Fabric, and PySpark.
• Demonstrate strong understanding of data architecture, ETL/ELT pipeline development, data modeling, and production data systems.