CPW Data Engineer II

General Mills, Inc.; Mumbai; On-site; INR 1,400,000 - 2,200,000; Full time

Job summary

General Mills, Inc.는 글로벌 분석과 의사결정을 지원하는 확장 가능한 데이터 플랫폼 개발을 담당할 데이터 엔지니어 II를 찾습니다. Snowflake, dbt, Azure를 활용해 데이터 파이프라인을 설계하고 데이터 품질과 거버넌스를 유지합니다.

다양한 소스에서 모델링을 수행하고 대용량 데이터 처리에 능숙한 전문가를 선호합니다. 이해관계자와의 협업을 통해 데이터 자산의 성장과 가치를 높이게 됩니다.

Qualifications

• 데이터 엔지니어링 분야 최소 7년 경력 필요.
• 다양한 소스에서 데이터 파이프라인 설계·구현 경험 필수.
• Snowflake, dbt, Azure 등 데이터 플랫폼 도구에 능숙할 것.
• 데이터 웨어하우징 및 ETL/ELT 프레임워크에 대한 강한 이해.
• 대규모 데이터 처리 및 분석 엔지니어링 경험 보유.
• 데이터 품질, 거버넌스, 보안 원칙에 대한 이해 필요.

Responsibilities

• Snowflake, SQL, dbt, Azure를 이용한 ETL/ELT 파이프라인 설계 및 유지보수.
• Bronze/Silver/Gold 계층의 데이터 아키텍처 구축 및 운영.
• 다양한 데이터 소스(Nielsen, Circana 등) 통합 모델링 및 표준화.
• 데이터 거버넌스, 품질 모니터링 및 보안 정책 적용.
• 데이터 자산 신규 도입 시 구조/품질/유용성 평가 및 통합 설계.
• 스테이크홀더와의 요구사항 구체화 및 솔루션 전달.

Skills

SQL

데이터 파이프라인

커뮤니케이션

문제해결

데이터 관리

데이터 품질

Stakeholder 관리

Education

Bachelors in CS/IT/EE

석사 우대

Tools

Snowflake

dbt

Azure

Databricks

Job description

회사 개요

우리는 전 세계가 사랑하는 식품을 만들기 위해 존재합니다. 하지만 우리는 그 이상을 합니다. 우리 회사는 선한 영향력을 발휘하는 것을 최우선으로 하는 곳이며, 매일매일 배움을 확장하고 새로운 시각을 탐구하며 새로운 가능성을 재창조하는 공간입니다. 우리는 서로에게 도전하고 성장할 수 있는 대담한 사고를 가진 인재, 즉 최선을 다할 수 있는 인재를 찾습니다. 식품 분야의 확고한 리더가 되기 위해, 우리는 끊임없이 다음을 향해 도전하는 사람들과 함께합니다.

OVERVIEW

Cereal Partners Worldwide (CPW) is a joint venture between General Mills and Nestlé, two of the world’s leading food organizations. CPW combines the scale and capabilities of large organizations with the agility of a smaller, entrepreneurial business. The Data Engineer II will design, develop, and optimize scalable data assets and data platforms that support global analytics, reporting, and business decision‑making. The role will work across modern data technologies, including Snowflake, dbt, Azure, and Databricks, while ensuring strong data quality, governance, security, and performance.

KEY ACCOUNTABILITIES

Data Pipeline Development and Architecture: Design, develop, and maintain scalable ETL/ELT pipelines using Snowflake, SQL, dbt, Azure, and related technologies. Build and maintain robust data architectures supporting Bronze, Silver, and Gold data layers. Develop reliable data-processing workflows using incremental processing, deduplication, testing, and automation. Create reusable, maintainable, and well‑documented data engineering solutions.

Data Integration, Modeling, and Harmonization: Integrate data from multiple sources, including Nielsen, Circana, internal systems, and other business datasets. Develop scalable data models for reporting, analytics, and business intelligence use cases. Ensure consistency across product, period, market, customer, and other business dimensions and hierarchies. Review existing data models and processes to identify sustainable, scalable, and automated improvements. Harmonize data and processes while balancing speed, quality, and business requirements.

Data Governance, Quality, and Security — 15%: Establish and maintain data‑quality rules, validation metrics, monitoring processes, and issue‑resolution workflows. Ensure accurate, complete, consistent, and reliable data flows across the data environment. Support data governance, stewardship, metadata, documentation, and data‑security practices. Apply appropriate Snowflake security controls, including role‑based access, masking policies, and row‑access policies. Proactively identify and resolve data‑quality and data‑integrity issues.

New Data Asset Integration: Develop an understanding of new datasets requested by business stakeholders. Assess the structure, quality, and usability of new data sources. Design and implement processes to integrate new data assets into the existing data environment. Ensure that new data assets are scalable, governed, documented, and fit for analytics use.

Stakeholder and Analytics Enablement: Understand data requirements from stakeholders and internal teams. Deliver data transformations and analytical datasets that help answer business questions faster and more effectively. Support reporting and visualization teams with backend data architecture and data‑model development. Translate business requirements into practical and sustainable technical solutions. Communicate effectively with stakeholders, delivery teams, and business partners throughout the project lifecycle.

Performance, Scalability, and Cost Optimization: Optimize SQL queries, dbt models, Snowflake workloads, Delta storage formats, and pipeline performance. Apply performance‑tuning techniques across Snowflake, dbt, Databricks, and Azure environments. Design solutions that support scalability, reliability, maintainability, and cost efficiency. Monitor data workflows and proactively address performance and operational issues.

Continuous Improvement and Team Contribution: Contribute to continuous‑improvement initiatives across data engineering and analytics processes. Share knowledge, provide peer support, and promote effective engineering practices. Remain curious and adapt to evolving tools, technologies, and business needs. Build strong working relationships and contribute positively as a team member.

MINIMUM QUALIFICATIONS

• Bachelor’s degree in Computer Science, Information Technology, Electronics and Telecommunications, or a related field.
• Minimum 7 years of experience in Data Engineering.
• Mandatory experience working with data lakes and multiple data sources.
• Strong hands‑on experience with - SQL, Snowflake, Snowpipe, Snowflake Streams and Tasks, Dynamic Tables, Stored Procedures, dbt, including models, Jinja templating, macros, tests, and documentation.
• Strong knowledge of data warehousing, data modeling, and ETL/ELT frameworks.
• Experience with large‑scale data processing and analytics engineering.
• Experience developing data platforms or business intelligence solutions.
• Strong understanding of data quality, governance, security, and data‑access principles.
• Effective communication, stakeholder‑management, and problem‑solving skills.
• Ability to manage ambiguity, make timely decisions, and deliver high‑quality work within agreed timelines.

PREFERRED SKILLS

• Master’s degree in Computer Science, Information Technology, Electronics and Telecommunications, or a related field.
• Experience in the FMCG, consumer goods, retail, or market research industries.
• Experience working with Nielsen, Circana, panel data, retail measurement data, or similar datasets.
• Experience with Snowpark, particularly Python, for complex transformation logic beyond standard SQL.
• Experience with Azure Data Factory, Azure Storage Accounts, Azure Key Vault, and Azure DevOps.
• Experience with CI/CD implementation for Snowflake and dbt deployments.
• Experience migrating data pipelines from Databricks to Snowflake, including Delta Live Tables and Unity Catalog.
• Familiarity with Snowflake RBAC, masking policies, row‑access policies, and other security frameworks.
• Experience with Databricks, PySpark, Delta Lake, or Azure Databricks.
• Knowledge of PowerBI or other business intelligence and visualization platforms.
• Relevant certifications in Snowflake, dbt, Azure, or data engineering are desirable.
• Continuous‑improvement mindset with a strong focus on data accuracy and reliability.
• Ability to build effective relationships, influence stakeholders, and collaborate across global teams.
• Demonstrated ownership, attention to detail, curiosity, and commitment to delivering outstanding results.

자격 요건

지원자는 해당 직무가 위치한 국가의 최소 연령 요건을 충족해야 합니다.

새로운 가능성. 대담한 사고. 진실된 마음. 탁월한 사람들과 함께 탁월한 브랜드를 만듭니다. General Mills는 전 세계가 사랑하는 식품을 만듭니다. 100여 개 시장에 걸쳐서 우리는 힘을 합쳐 새로운 제품을 개발하고, 혁신을 주도하며, 함께 성장해 나갑니다. 이곳에서 여러분은 선의를 추구하는 기업에서 의미 있는 일을 하며 식품 산업의 미래를 이끌어 나갈 수 있습니다.