CPW Data Engineer II at General Mills in Powai

Job description

COMPANY OVERVIEW We exist to make food the world loves. But we do more than that. Our company is a place that prioritizes being a force for good, a place to expand learning, explore new perspectives and reimagine new possibilities, every day. We look for people who want to bring their best — bold thinkers with big hearts who challenge one another and grow together. Because becoming the undisputed leader in food means surrounding ourselves with people who are hungry for what’s next.​ 
 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 Contrib