Data Engineer 2

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 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 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.




ELIGIBILITY

Applicants must meet minimum age qualifications in the country in which the job is located.