Lead Data Engineer

General Mills

. Act as a data and analytics leader guiding engineering decisions and delivery outcomes .

Posted 11/5/2026full-timeMumbai • IndiaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive expertise in data engineering and architecture, with strong capabilities in designing scalable data pipelines and implementing data quality frameworks. Proficient in modern cloud platforms, particularly Google Cloud, and skilled in SQL, Python, and ETL processes.

Highest-signal resume keywords

Data EngineeringCloud PlatformsSQLPythonData Modelling

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringData ArchitectureETL ProcessesData WarehousingData QualityData ObservabilityData ModellingOrchestrationPrompt EngineeringCoding Skills

Soft Skills

Problem-SolvingAnalytical SkillsEffective CommunicationCollaborationAdaptability

Tools & Technologies

Google CloudBigQueryComposerDataflowGitGitHub ActionsJiraAzure DevOpsKafkaDistributed Computing

Industry Keywords

Data EcosystemCloud TechnologiesAI in Data EngineeringTechnical ArchitectureData Pipelines

Tech Stack

Tools & technologies

AzureBigQueryCloudETLKafkaPythonSQL

About the role

Key responsibilities & impact

Act as a data and analytics leader guiding engineering decisions and delivery outcomes
Lead project team members to deliver data capabilities
Partner with business analysts and architects to define technical architectures for strategic projects and initiatives
Evaluate, implement, and deploy emerging data engineering technologies, processes, and practices
Make pipeline architecture decisions based on standardized patterns and technologies
Lead the design and implementation of scalable data pipelines, data modelling, and data quality frameworks
Troubleshoot technical and performance issues and research root causes in the cloud data ecosystem
Contribute work to the broader GMI data community and collaborate on refining common libraries
Mentor and guide data engineers, fostering innovation, technical excellence, and strong engineering practices
Assist in hiring and onboarding

Requirements

What you’ll need

Bachelor’s degree or equivalent practical experience in a relevant field
12+ years of experience in data engineering, data architecture, or related technology roles
Strong experience with modern cloud platforms, preferably Google Cloud
Hands-on experience in ETL processes, data warehousing, data quality, data observability, and structured and unstructured data systems
Ability to work directly with business partners to design solutions and explain architecture trade-offs clearly
Strong technical skills in SQL, Python, BigQuery, Composer, Dataflow, and dbt
Experience in Data Modelling, Data Observability, Orchestration, and Prompt Engineering
Familiarity with Git, GitHub Actions, Jira or Azure DevOps, and Agile ways of working
Strong problem-solving and analytical skills with attention to detail
Effective verbal and written communication skills, including the ability to explain technical concepts to non-technical stakeholders
Collaborative, adaptable, self-driven, and able to take ownership from project initiation through completion
Strong hands-on coding skills in Python and SQL
Experience developing procedures and scripts for data loading, transformation, and migration
Familiarity with Kafka and distributed computing tools
Broader experience with cloud technologies, with Google Cloud preferred
Strong awareness of AI and its application in data engineering
Applicants must meet minimum age qualifications in the country in which the job is located

Benefits

Comp & perks

Learning and expansion of perspectives
Mentorship and technical talent development opportunities
Collaborative work environment
Opportunity to influence data strategy and modernize the data engineering ecosystem