Job Description
Role overview
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
Team leadership & delivery
- 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
Domain expertise
- 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
Individual skills
- 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
Location(s)
Mexico City - Antara Tower A - 5th Floor - Local Office
Kraft Heinz is an Equal Opportunity Employer – Underrepresented Ethnic Minority Groups/Women/Veterans/Individuals with Disabilities/Sexual Orientation/Gender Identity and other protected classes.