Group Lead, IT - Data Engineering DataOps

Job Description

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

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

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

Location(s)

Bengaluru - Brookfield GCC

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