Manager Data Engineer

Kraft Heinz; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Kraft Heinz in Bengaluru seeks a Manager — Data Engineering to lead a cross-functional delivery team, owning architecture decisions end-to-end and driving data pipelines for analytics. You will mentor engineers, oversee scrum ceremonies, and collaborate with stakeholders to deliver scalable data solutions.

The role emphasizes building enterprise-grade data platforms, governance, and the adoption of AI/LLM-based tooling to elevate data quality and delivery velocity.

Qualifications

• Bachelor's degree or higher in CS, statistics, IT, or related field.
• 5+ years of data engineering experience with at least 2 years in a technical lead role.
• Proven track record delivering data pipelines and analytics assets.

Responsibilities

• Lead and manage a team of data engineers, providing technical guidance and mentorship.
• Own architecture decisions end-to-end for projects and lead design leadership.
• Oversee day-to-day team activities, including scrum ceremonies and backlog management.
• Develop and operate modern data engineering solutions: ingestion, processing, integration, governance.
• Collaborate with stakeholders to identify business needs and deliver data-driven solutions.

Skills

Python

SQL

Data modelling

Dagster

dbt

Snowflake

Cloud platforms

Agile

Data governance

Education

Bachelor's degree or higher in Computer Science, Statistics, Business, Information Technology, or a related field

Tools

Azure

AWS

GCP

Job description

As a Manager — Data Engineering, you will lead a cross-functional delivery team in building and maintaining the framework and platform capabilities that drive our data pipelines and analytics solutions. You will take design ownership of individual projects, run day-to-day team activities, and contribute to community-of-practice enablement and onboarding programs. Your team will work closely with stakeholders across the organisation to identify and mitigate data challenges and to create data assets that drive measurable business value.

Primary responsibilities

• Lead and manage a team of data engineers, providing technical guidance and mentorship to ensure their growth and development
• Take design leadership over individual projects — own architecture decisions end-to-end, not just contribute
• Take charge of day-to-day team activities including scrum ceremonies, sprint planning, and backlog management
• Oversee the development and operation of modern data engineering solutions, including data ingestion, processing, integration, and governance
• Collaborate with stakeholders to identify business needs and develop solutions that meet those needs

Community of practice contribution

• Develop and maintain shared frameworks for data engineering; contributions should reflect design for broader organisational scope, not just the immediate team
• Contribute to the onboarding of new data engineers, analysts, product owners, and other team members joining the project
• Contribute to community-of-practice enablement programs — including internal upskilling and certification, training frameworks, and knowledge-sharing initiatives
• Act as a data owner and functional subject matter expert for assigned areas, supporting data product certification and lineage maturity

Platform & operations

• Ensure platform stability and operational SLAs; drive reduction in operational noise and manual intervention
• Extend DevOps capabilities for deploying and operating data solutions
• Work closely with product owners and stakeholders to identify and mitigate potential data challenges
• Support the adoption of AI and LLM-based tooling to improve engineering efficiency and data quality

Qualifications

Education

• Bachelor's degree or higher in Computer Science, Statistics, Business, Information Technology, or a related field

Experience

• 5+ years of experience in data engineering or a related discipline, with at least 2 years in a technical lead or team lead capacity
• Proven track record delivering and supporting software and data engineering capabilities in a fast-paced, dynamic environment
• Experience contributing to shared frameworks within the data domain, and creating data assets used in mission-critical applications

Technical skills

• Intermediate data design skills — data modelling, schema design, and pipeline architecture for enterprise-scale solutions
• Core Python proficiency — including object-oriented programming, reusable library design, and clean scalable code; not just ad hoc scripting
• Advanced SQL — window functions, query optimisation, and complex transformation logic beyond basic CRUD operations; experience with dbt is a plus
• Intermediate DevOps and cloud (Azure, AWS, or GCP) — including CI/CD pipeline ownership, deployment practices, and cloud cost awareness
• Experience with Agile methodologies; hands-on experience running scrum ceremonies
• Experience with automated testing and data testing frameworks — able to design and enforce test coverage across pipelines and data assets
• 5+ years building enterprise data solutions with a proven track record delivering high-quality pipelines and analytics products
• Familiarity with modern orchestration and transformation tooling — Dagster, dbt, and Snowflake experience strongly preferred
• Understanding of data warehousing concepts and architecture patterns such as medallion architecture and dimensional modelling
• Awareness of data product concepts including lineage, certification, and operational telemetry
• Experience implementing data quality frameworks — including validation, profiling, and monitoring — to ensure integrity and consistency across data systems
• Experience working in environments where data engineering capabilities are shared as platform services, not built in isolation
• Strong collaborator and team player, with the ability to work effectively with business stakeholders and cross-functional teams
• Strong analytical thinker — able to troubleshoot complex pipeline issues, optimise performance, and identify improvement opportunities across data systems
• Clear point of view on data engineering best practices — and the ability to bring others along, not just hold the opinion
• Effective communicator who can translate technical decisions into business language

Mindsets and behaviours

• Embraces change and is passionate about driving innovation and continuous improvement
• Believes in a non-hierarchical culture of collaboration, transparency, safety, and trust
• Not afraid to take risks and try new approaches; willing to learn from failure and use it to drive growth
• Invested in the growth of others — sees enabling teammates as part of their own success