Manager, Data & Analytics Engineering - 11513

India·HyderabadRemoteMid-Senior Levelsenior

OtherData & Analytics

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Coupa makes margins multiply through its community-generated AI and industry-leading total spend management platform for businesses large and small. Coupa AI is informed by trillions of dollars of direct and indirect spend data across a global network of 10M+ buyers and suppliers. We empower you with the ability to predict, prescribe, and automate smarter, more profitable business decisions to improve operating margins.

Why join Coupa?

🔹 Pioneering Technology: At Coupa, we're at the forefront of innovation, leveraging the latest technology to empower our customers with greater efficiency and visibility in their spend.

🔹 Collaborative Culture: We value collaboration and teamwork, and our culture is driven by transparency, openness, and a shared commitment to excellence.

🔹 Global Impact: Join a company where your work has a global, measurable impact on our clients, the business, and each other.

Learn more on Life at Coupa blog and hear from our employees about their experiences working at Coupa.

The Impact of a Manager, Data & Analytics Engineering at Coupa:

We are seeking a strong Manager of Data & Analytics Engineering to lead a team of data and analytics engineers supporting our Product and Customer Value organizations. This India-based role is responsible for the curation, transformation, and delivery of trusted data assets—ensuring stakeholders have access to certified models and polished BI deliverables (Tableau, Sigma) that drive informed decision-making. The team may include members across multiple geographies over time, so comfort operating in a distributed environment is important.

The ideal candidate combines strong technical fluency in modern data stack technologies (dbt, Snowflake, SQL) with the ability to step into a leadership role, guide a team, and partner closely with business stakeholders and BI teams. This is an excellent opportunity for a senior individual contributor or technical lead ready to take on people management responsibilities while continuing to drive hands-on engineering excellence

Lead, mentor, and grow a team of data and analytics engineers, fostering a collaborative, high-performance culture focused on continuous improvement.

Provide technical direction, career development support, and hands-on guidance to ensure team excellence and individual growth.

Drive hiring and onboarding to scale the team in alignment with organizational growth and evolving business needs.

Establish clear performance expectations and delivery standards, ensuring accountability and quality across the team.

Coordinate effectively across time zones and geographies as the team composition evolves.

Own the curation, transformation, and delivery of analytics-ready data models supporting Product and Customer Value use cases.

Champion dbt best practices including modular design, rigorous testing, comprehensive documentation, and governance tagging to ensure all models meet certification standards.

Collaborate with data architects and upstream data engineering teams to ensure source data quality and alignment with downstream modeling needs.

Build and maintain trusted, governed data assets that serve as a single source of truth for BI and analytics consumers.

Institutionalize engineering standards for code reviews, CI/CD, monitoring, and proactive incident management within the team.

Oversee the development and delivery of Tableau and Sigma dashboards that meet business requirements and enhance stakeholder decision-making.

Ensure data models are optimized for downstream visualization, enabling effective governed self-service analytics.

Translate complex business requirements from Product and Customer Value stakeholders into well-structured data solutions and BI deliverables.

Support governance and data integrity across all analytics processes, ensuring adherence to organizational data standards and access controls.

Build and maintain strong relationships with Product and Customer Value leadership, acting as a trusted partner for data-driven decision-making.

Communicate technical roadmaps, risks, trade-offs, and delivery timelines clearly to both technical and non-technical audiences.

Evangelize a data-driven culture by ensuring data assets are discoverable, well-documented, and championed across the teams you support.

Collaborate cross-functionally with other data engineering teams, data governance, and platform teams to ensure alignment and consistency.

Experience: 6+ years in Data/Analytics Engineering, with demonstrated ability to lead technical projects and guide other engineers.

Modern Stack Expertise: Strong hands-on proficiency with SQL, dbt, and cloud data warehouses (Snowflake strongly preferred).

BI Proficiency: Experience building and delivering enterprise dashboards in Tableau and/or Sigma, with an understanding of how data modeling decisions impact visualization and self-service analytics.

Engineering Standards: Experience with code review processes, CI/CD pipelines, testing frameworks, and documentation practices.

Governance Knowledge: Strong understanding of data governance principles, data integrity, and access control best practices.

Stakeholder Management: Excellent interpersonal and communication skills with a proven ability to partner with business stakeholders and translate requirements into technical solutions.

Leadership Potential: A collaborative mindset with the desire and aptitude to step into people management, mentor engineers, and build team culture.

Self-Starter: Creative, proactive mindset with the ability to work independently and manage priorities across multiple workstreams.

Education: Bachelor’s degree in Computer Science, Engineering, Data Analytics, Mathematics, or a related technical field (or equivalent professional experience).

Prior experience in a technical lead or team lead capacity, even without formal management title.

Experience supporting Product and/or Customer Success/Value business domains.

Familiarity with data cataloging and metadata management tools (e.g., Atlan, Alation).

Experience working in globally distributed teams with offshore/onshore collaboration models.

Exposure to data quality frameworks and observability tooling.

Track record of driving model deprecation, optimization, or cost-reduction initiatives in large dbt environments.