Data Engineer Manager - AWS Databricks

Jobgether; India; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Jobgether partners to fill a Data Engineer Manager - AWS Databricks role based in India. This hands-on leadership position focuses on designing, scaling, and optimizing modern data platforms to support Journey Analytics initiatives.

You will lead a team of data engineers, contribute to architecture, and drive technical standards across pipelines, data models, and GitHub workflows, aligning solutions with business priorities.

Qualifications

• 7+ years of professional data engineering experience with scalable platforms.
• Experience leading or mentoring data engineering teams.
• Hands-on Databricks experience for pipelines and modeling.
• Experience with AWS and cloud-based data environments is valuable.

Responsibilities

• Lead, mentor, and support a team of data engineers while participating in development, architecture, reviews, and technical decision-making.
• Define and execute the technical strategy for Journey Analytics data platforms.
• Design, build, and maintain scalable data pipelines using Databricks.
• Develop modular data components to support multiple journeys and reduce duplication.
• Manage GitHub repositories, promote version-control standards and workflows.
• Collaborate with analytics, product, and engineering stakeholders to align solutions with priorities.

Skills

Databricks

AWS

Data pipelines

Data modeling

GitHub

Leadership

Mentoring

Git workflows

Version control

Tools

Databricks

GitHub

AWS

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Engineer Manager - AWS Databricks based in India.

This is a hands-on leadership role focused on designing, scaling, and optimizing modern data platforms supporting Journey Analytics initiatives. You will lead a team of data engineers while remaining deeply involved in architecture, development, and technical decision-making. The role combines people leadership with hands-on engineering across Databricks, data pipelines, data modeling, and GitHub-based development workflows. You will establish technical standards that improve code quality, scalability, reusability, and reliability. Working closely with analytics, product, and engineering stakeholders, you will translate business needs into robust data solutions. This is an opportunity to shape technical direction while driving meaningful improvements across data architecture and engineering practices.

Accountabilities

• Lead, mentor, and support a team of data engineers while remaining actively involved in development, architecture, code reviews, and technical decision-making.
• Define and execute the technical strategy for Journey Analytics data platforms, balancing long-term architecture with practical delivery needs.
• Design, build, and maintain scalable, automated data pipelines using Databricks, ensuring reliable downstream data consumption.
• Develop modular and reusable data components that can support multiple customer or business journeys while reducing duplication and improving maintainability.
• Design and evolve scalable data models for analytics, reporting, and future data use cases.
• Manage and optimize GitHub repositories, promoting strong version-control practices, coding standards, documentation, and development workflows.
• Lead and participate in refactoring legacy codebases to improve scalability, maintainability, performance, and reusability.
• Establish and uphold standards for data quality, governance, performance, reliability, and engineering excellence across pipelines and datasets.
• Collaborate with analytics, product, engineering, and other cross-functional stakeholders to align technical solutions with business priorities.
• Identify technical risks, bottlenecks, and opportunities for improvement, taking ownership of mitigation and optimization initiatives.
• Drive continuous improvement across data processes, engineering practices, automation, and documentation.

Requirements

• 7+ years of professional experience in data engineering, with a strong track record of designing and delivering scalable data solutions.
• Demonstrated experience leading or mentoring data engineering teams while maintaining strong hands-on technical involvement.
• Hands-on expertise developing, maintaining, and optimizing data pipelines in Databricks.
• Strong experience with GitHub repositories, Git workflows, version control, and collaborative software development practices.
• Proven ability to refactor and maintain legacy codebases, improving their maintainability, scalability, and reusability.
• Strong understanding of data modeling and the design of reusable data components for analytics and reporting.
• Experience building reliable, scalable data platforms with a strong focus on data quality, performance, governance, and operational reliability.
• Ability to work effectively across analytics, product, and engineering teams and translate business requirements into practical technical solutions.
• Strong ownership mindset, with the ability to independently drive initiatives from high-level direction through execution with minimal supervision.
• Strong problem-solving, communication, mentoring, and technical leadership skills.
• Experience with AWS and cloud-based data engineering environments is valuable for this role.
• Experience using Databricks Genie is considered a plus.

Benefits

• Opportunity to lead and mentor a data engineering team while remaining hands-on with modern data technologies.
• Exposure to large-scale Journey Analytics initiatives and complex, cross-functional data challenges.
• Opportunity to influence data architecture, engineering standards, and technical strategy.
• Hands-on work with Databricks, automated data pipelines, scalable data models, and modern development workflows.
• A collaborative environment involving analytics, product, and engineering stakeholders.
• Opportunities for continuous technical learning and professional development.
• Ability to drive meaningful improvements in data quality, reliability, scalability, and engineering efficiency.
• Confidential handling of candidate information in accordance with applicable equal employment opportunity guidelines.