Senior Manager, Data Engineering & DataOps

Position Summary

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

• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field (Master's 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

Experiences

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

Location(s)

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