About the role

Data project management

Own end-to-end delivery of data initiatives, translating strategy into executable plans with clear milestones, timelines, and measurable outcomes.
Project Planning: Lead planning, estimation, and execution of data engineering projects.
Drive execution discipline by managing sprint cycles, tracking progress, resolving blockers, and ensuring on-time, high-quality delivery.
Coordinate cross-functional teams (engineering, analytics, business) to align priorities, manage dependencies, and maintain delivery momentum.
Ensure data quality, governance, and reliability standards are met throughout the delivery lifecycle, minimizing risks and rework.
Communicate status, risks, and outcomes to stakeholders, enabling quick decisions and continuous improvement in delivery performance.

Project & Stakeholder Management

Client Engagement: Understand client requirements and translate them into technical deliverables.
Cross-functional Collaboration: Work closely with data analysts, data scientists, software engineers, and business stakeholders.

Team Leadership

Performance Management: Conduct regular check-ins, provide feedback, and contribute to performance evaluations.
Resource Planning: Identify skill gaps and support hiring efforts or contractor onboarding when needed.
Mentorship: Guide junior engineers and foster a culture of learning and continuous improvement.

Data strategy

Lead data strategy creation and implementation that helps the organization to effectively collect, manage, use, and protect its data to support business goals and drive value.
Oversee the design and implementation of modern data platforms.
Collaborate with stakeholders to define and maintain a clear data roadmap that outlines future data capabilities, technology adoption, and process improvements.

Department: Data management, SGU

Reports to: Data management SGU lead (VP)

Responsibility Level: AVP/SAVP/VP

Span of Control: 50+ team (direct and indirect)

Positions reporting into this role: engineers, managers, AVP

Location: India/US

Lead architecture & design: Define scalable and secure data architecture for data pipelines, warehousing, and processing systems.
Technology Selection: Evaluate and recommend appropriate tools, platforms (e.g., Spark, Kafka, Airflow, Snowflake), and cloud services (AWS, GCP, Azure).
Code Review & Best Practices: Enforce coding standards, review pull requests, and mentor team members on clean and efficient code.

ETL/ELT Implementation: Lead development of ETL/ELT processes to extract, transform, and load data from multiple sources.
Batch & Streaming Processing: Design and implement both batch and real-time data processing pipelines.
Data Integration: Work with APIs, third-party data sources, and internal systems to integrate disparate data.

Data Quality Assurance: Implement checks to ensure data completeness, accuracy, and timeliness.
Metadata Management: Oversee metadata standards and lineage tracking for transparency and auditability.
Compliance: Ensure systems and processes comply with data privacy and security standards (GDPR, HIPAA, etc.).

Lead the development and management of Requests for Proposals (RFPs) and Statements of Work (SOWs) for data management projects.
Ensure all proposals and contracts meet organizational standards and client requirements.

Understand data ecosystem – data strategy aligning with business goals, data governance, quality, medallion architecture, and so on.
Oversee the design and implementation of modern data platforms.
Manage proposals and projects involving cloud service providers (e.g., AWS, Azure, Google Cloud).
Ensure the efficient and secure integration of cloud services with existing data systems.
Optimize cloud-based data storage and processing solutions for performance and cost-effectiveness.
Software Engineering Project Delivery: Leverage engineering expertise to oversee the delivery of enterprise software projects for global customers.

Experience with the key elements of data lifecycle while designing and recommending data solutions to clients:

Scalable Data Architectures: Scalable and efficient data solutions that meet client needs.
Data Integration: Integration of various data sources, ensuring that data is collected, processed, and made available in a structured manner.
Data Security and Compliance: Ensure that data systems comply with relevant security and privacy regulations, including GDPR, HIPAA, etc., and are secure from threats.
Data Pipeline Management: Design, development, and optimization of data pipelines to ensure high-quality, efficient data flow and storage.
Performance Optimization: Continuously monitor and optimize data workflows, addressing bottlenecks and improving data processing speed.
Ensure Data Quality: Robust data validation and quality checks to ensure the accuracy, consistency, and completeness of the data.
Client-Facing Data Products: Development of client-facing data products or reports, ensuring that the data delivered is valuable, actionable, and easy to use for clients.
Data Governance: Establish data governance frameworks, policies, and processes to ensure proper data stewardship, security, privacy, and compliance (e.g., GDPR, HIPAA).
Data Lineage and Documentation: Documentation of data flows, transformations, and lineage to ensure traceability and accountability.

Bachelor’s or Master’s degree in Data Science, Information Technology, Business Administration, or a related field.
Proven experience in winning accounts within the data management domain or similar industries.
Strong knowledge of data governance, integration technologies, security protocols, and quality assurance methodologies.
Excellent communication skills to engage clients and present complex solutions effectively.
Demonstrated ability to lead teams and manage multiple projects simultaneously.

SAVP - Data Delivery Lead

About the role