OVERVIEW

Cereal Partners Worldwide (CPW) is a joint venture between General Mills and Nestl, two of the worlds 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 setting.
- 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

- Bachelors 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

- Masters 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 Power BI 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.

📌 CPW Data Engineer (Mumbai)
🏢 General Mills
📍 Mumbai