Mid-Level
Commercial Data Engineer
Overview
The Commercial Data Engineer is responsible for building and maintaining scalable data pipelines and platforms that enable commercial reporting, analytics, forecasting, and business decision-making. This role integrates data from multiple sources to deliver reliable, high-quality datasets for Sales, Commercial Analytics, Revenue Management, Category Management, and Supply Chain teams. Working closely with business and technology stakeholders, the role ensures data availability, quality, and performance to support commercial growth and data-driven initiatives.
Responsibilities
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Design, develop, and maintain scalable data pipelines to support commercial reporting, analytics, forecasting, and business intelligence.
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Integrate data from multiple sources, including distributor systems, point-of-sale (POS), ERP systems such as SAP, CRM platforms, shipment records, customer master data, and syndicated market data.
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Build and maintain ETL/ELT workflows, data models, and datasets to support sales performance reporting, market share analysis, revenue growth management, trade promotion analytics, customer performance, and demand forecasting.
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Implement automated data validation, monitoring, reconciliation, and alerting processes to ensure data accuracy, completeness, consistency, and reliability.
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Develop and optimize data warehouse and data lake solutions to support scalable data processing and analytics requirements.
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Deliver reliable and optimized datasets for Power BI, Tableau, and other reporting and visualization tools.
Qualifications
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Bachelor's degree in Computer Science, Data Engineering, Information Systems, Analytics, or a related field.
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3–5 years of experience in Data Engineering, Analytics Engineering, or Business Intelligence, with hands-on experience in data pipeline development.
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Strong SQL development skills and experience building and maintaining ETL/ELT pipelines.
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Solid understanding of data warehousing, data modeling, and data integration concepts.
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Experience with cloud-based data platforms and modern data infrastructure.
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Familiarity with data visualization tools such as Power BI or Tableau and the ability to support analytics and reporting requirements.
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Experience implementing data quality checks, monitoring, and governance practices.
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