Group Lead, IT - Data Engineering DataOps

About this role

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Skills you'll use

What you'll do

• Group Lead – 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

Who can apply

• 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

About Kraft Heinz

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