{"id":200422,"url":"https://alion.io/job/general-mills-cpw-data-engineer-ii","title":"CPW Data Engineer II","company":{"id":93250,"name":"General Mills","domain":"generalmills.com","url":"https://alion.io/company/general-mills","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":100,"open_postings":6,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":37,"computed_at":"2026-10-20T05:45:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mumbai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":15000,"max_usd":38000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":21},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Azure Data Factory","optional":true},{"name":"Azure DevOps","optional":true},{"name":"CI/CD","optional":true},{"name":"Delta Lake","optional":true},{"name":"Power BI","optional":true},{"name":"pySpark","optional":true},{"name":"Python","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-08-13T00:00:00Z","employer_posted_date":"2026-10-13","last_verified_at":"2026-10-20T22:50:09Z","board_verified":true,"closed_at":null,"days_open":69,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":68},"description":"COMPANY OVERVIEW\nWe 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.\n\nOVERVIEW\nCereal 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.\nThe 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.\nKEY ACCOUNTABILITIES\nData Pipeline Development and Architecture :\nDesign, develop, and maintain scalable ETL/ELT pipelines using Snowflake, SQL, dbt, Azure, and related technologies.\nBuild and maintain robust data architectures supporting Bronze, Silver, and Gold data layers.\nDevelop reliable data-processing workflows using incremental processing, deduplication, testing, and automation.\nCreate reusable, maintainable, and well-documented data engineering solutions.\nData Integration, Modeling, and Harmonization\nIntegrate data from multiple sources, including Nielsen, Circana, internal systems, and other business datasets.\nDevelop scalable data models for reporting, analytics, and business intelligence use cases.\nEnsure consistency across product, period, market, customer, and other business dimensions and hierarchies.\nReview existing data models and processes to identify sustainable, scalable, and automated improvements.\nHarmonize data and processes while balancing speed, quality, and business requirements.\nData Governance, Quality, and Security - 15%\nEstablish and maintain data-quality rules, validation metrics, monitoring processes, and issue-resolution workflows.\nEnsure accurate, complete, consistent, and reliable data flows across the data environment.\nSupport data governance, stewardship, metadata, documentation, and data-security practices.\nApply appropriate Snowflake security controls, including role-based access, masking policies, and row-access policies.\nProactively identify and resolve data-quality and data-integrity issues.\nNew Data Asset Integration\nDevelop an understanding of new datasets requested by business stakeholders.\nAssess the structure, quality, and usability of new data sources.\nDesign and implement processes to integrate new data assets into the existing data environment.\nEnsure that new data assets are scalable, governed, documented, and fit for analytics use.\nStakeholder and Analytics Enablement\nUnderstand data requirements from stakeholders and internal teams.\nDeliver data transformations and analytical datasets that help answer business questions faster and more effectively.\nSupport reporting and visualization teams with backend data architecture and data-model development.\nTranslate business requirements into practical and sustainable technical solutions.\nCommunicate effectively with stakeholders, delivery teams, and business partners throughout the project lifecycle.\nPerformance, Scalability, and Cost Optimization\nOptimize SQL queries, dbt models, Snowflake workloads, Delta storage formats, and pipeline performance.\nApply performance-tuning techniques across Snowflake, dbt, Databricks, and Azure environments.\nDesign solutions that support scalability, reliability, maintainability, and cost efficiency.\nMonitor data workflows and proactively address performance and operational issues.\nContinuous Improvement and Team Contribution\nContribute to continuous-improvement initiatives across data engineering and analytics processes.\nShare knowledge, provide peer support, and promote effective engineering practices.\nRemain curious and adapt to evolving tools, technologies, and business needs.\nBuild strong working relationships and contribute positively as a team member.\nMINIMUM QUALIFICATIONS\nBachelor’s degree in Computer Science, Information Technology, Electronics and Telecommunications, or a related field.\nMinimum 7 years of experience in Data Engineering.\nMandatory experience working with data lakes and multiple data sources.\nStrong hands-on experience with - SQL, Snowflake, Snowpipe, Snowflake Streams and Tasks, Dynamic Tables, Stored Procedures, dbt, including models, Jinja templating, macros, tests, and documentation\nStrong knowledge of data warehousing, data modeling, and ETL/ELT frameworks.\nExperience with large-scale data processing and analytics engineering.\nExperience developing data platforms or business intelligence solutions.\nStrong understanding of data quality, governance, security, and data-access principles.\nEffective communication, stakeholder-management, and problem-solving skills.\nAbility to manage ambiguity, make timely decisions, and deliver high-quality work within agreed timelines.\nPREFERRED SKILLS\nMaster’s degree in Computer Science, Information Technology, Electronics and Telecommunications, or a related field.\nExperience in the FMCG, consumer goods, retail, or market research industries.\nExperience working with Nielsen, Circana, panel data, retail measurement data, or similar datasets.\nExperience with Snowpark, particularly Python, for complex transformation logic beyond standard SQL.\nExperience with Azure Data Factory, Azure Storage Accounts, Azure Key Vault, and Azure DevOps.\nExperience with CI/CD implementation for Snowflake and dbt deployments.\nExperience migrating data pipelines from Databricks to Snowflake, including Delta Live Tables and Unity Catalog.\nFamiliarity with Snowflake RBAC, masking policies, row-access policies, and other security frameworks.\nExperience with Databricks, PySpark, Delta Lake, or Azure Databricks.\nKnowledge of Power BI or other business intelligence and visualization platforms.\nRelevant certifications in Snowflake, dbt, Azure, or data engineering are desirable.\nContinuous-improvement mindset with a strong focus on data accuracy and reliability.\nAbility to build effective relationships, influence stakeholders, and collaborate across global teams.\nDemonstrated ownership, attention to detail, curiosity, and commitment to delivering outstanding results.\n\nELIGIBILITY\nApplicants must meet minimum age qualifications in the country in which the job is 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