Senior Manager, Data Engineering & DataOps

Senior Manager Data Engineering / DataOps

Position Summary

The Kraft Heinz Information Technology organization is seeking a Group Lead for Data Engineering and DataOps who will serve as an anchor leader for operational excellence across our data platform. This role is primarily focused on the data operations discipline ensuring the reliability observability and continuous delivery of data products across the enterprise. Working closely with cross-functional delivery teams the Group Lead will drive DataOps culture and practice govern operational processes and lead a team of data engineers through the full Build Run Support lifecycle. At this level the leader owns the DataOps strategy for their group: pipeline reliability incident response frameworks data quality operations operational tooling and team development.

Primary Responsibilities

• Own the DataOps strategy and operational roadmap for the group covering pipeline reliability monitoring incident management and continuous delivery practices
• Manage allocation and day-to-day direction of data engineering resources focused on operational delivery across cross-functional teams
• Establish and enforce DataOps standards including SLAs SLOs alerting thresholds data quality checks and observability frameworks
• Lead incident response culture: define escalation paths conduct post-mortems and drive systemic remediation to reduce recurring pipeline failures
• Drive CI/CD adoption across data pipelines ensuring automated testing deployment and rollback capabilities are standard practice
• Hold engineering team members accountable for delivery tracking operational transparency and timely escalation using shared metrics and evidence
• Conduct and govern code and pipeline reviews at a high standard ensuring consistency resilience and maintainability across all teams
• Manage a backlog of platform and operational improvements to enhance pipeline efficiency security and cost optimization on the cloud analytics platform
• Oversee data quality operations: implement and maintain data quality frameworks manage data contracts and ensure downstream trust in data assets
• Collaborate with product owners stakeholders and data consumers to identify operational risks and proactively mitigate data reliability issues
• Lead hiring and onboarding for data engineering roles within the group with a focus on operational and platform engineering skills
• Design and deliver technical training programs focused on DataOps practices tooling and operational mindset for data engineers

Qualifications

• Bachelors degree in Computer Science Information Technology Engineering or a related field (Masters degree preferred)
• 6 years of experience in data engineering with significant focus on data operations platform reliability or DevOps/DataOps practices
• Proven experience leading teams and managing delivery in a fast-paced cross-functional environment
• Strong Python programming skills applied to pipeline development automation and operational tooling
• Expert SQL development with deep understanding of query performance and data reliability patterns
• Expert with automated data testing frameworks (Great Expectations dbt pytest) and CI/CD tooling (Azure DevOps GitHub Actions)
• Proven experience with data warehousing platforms (e.g. Snowflake BigQuery) and their operational management
• Solid experience with major cloud platforms and infrastructure (Azure preferred) including cost management and optimization
• Strong knowledge of pipeline orchestration tools (e.g. Airflow Azure Data Factory Prefect) and monitoring/alerting frameworks
• Familiarity with observability tooling (e.g. Datadog Azure Monitor) applied to data pipeline health
• Familiarity with BI tools (Tableau Power BI Looker) as downstream consumers of operational data products

Domain Expertise

• 6 years of experience delivering enterprise-level data solutions in production environments
• Demonstrated experience owning and improving the operational health of large-scale data platforms
• Proven ability to implement DataOps and DevOps practices (CI/CD IaC automated testing monitoring) in the data domain
• Experience managing data quality operations and enforcing data contracts across multiple consuming teams
• Experience with Agile and Kanban methodologies applied to operations and platform work

Experiences

• Experience leading and developing cross-functional data engineering teams
• Track record of reducing pipeline failure rates and improving mean time to resolution (MTTR) through systemic improvements
• Experience collaborating with platform cloud infrastructure and security teams to operationalize data pipelines at scale

Individual Skills

• Excellent communication skills with the ability to translate operational risks and metrics into business impact for non-technical stakeholders
• Strong analytical and problem-solving skills with a bias toward root cause resolution over workarounds
• Proven ability to build operational processes that scale across teams and geographies

Mindsets and Behaviors

• Passionate about operational excellence and the reliability engineering mindset applied to data
• Continuous learner with agility across both technical tooling and business process domains
• Self-starter who thrives in environments that reward initiative ownership and entrepreneurial thinking
• Believes in a culture of transparency psychological safety and evidence-based decision-making
• Committed to building team capability not just solving problems individually but raising the floor for the whole group

Kraft Heinz is an Equal Opportunity Employer Underrepresented Ethnic Minority Groups/Women/Veterans/Individuals with Disabilities/Sexual Orientation/Gender Identity and other protected classes.