Senior Data Engineer

Company Description 

Saatvik Agro is the agro-ingredient unit of the Saatvik Group, specializing in high-quality maize-based ingredients used in food, nutrition, animal feed, and industrial applications. The organization focuses on purity and scientific rigor, converting responsibly sourced maize into functional and reliable ingredient solutions. Its products are designed to meet the evolving needs of modern manufacturers who demand consistency, performance, and safety. Guided by the belief that better ingredients create better outcomes, Saatvik Agro aims to support customers in delivering superior products to their markets.

Role Description 

We are looking for a Senior Data Engineer to help build, manage, and improve reliable data systems that support business and operational decision-making across the organization.

The role is suitable for experienced professionals with backgrounds in data engineering, ETL/ELT, SQL, Python, cloud platforms, data warehousing, analytics engineering, or business intelligence.

The Senior Data Engineer will work with business, operations, finance, supply chain, production, marketing, and technology teams to build scalable data pipelines, improve data quality, integrate multiple data sources, and make reliable information available for reporting, analytics, and decision-making.

Qualifications

• Designing, building, and maintaining scalable data pipelines and ETL/ELT workflows.
• Integrating data from multiple internal and external systems.
• Developing and optimizing SQL queries, data models, and database structures.
• Maintaining reliable data warehouses, databases, and reporting datasets.
• Improving data quality, consistency, accuracy, and availability.
• Automating recurring data-processing and reporting workflows.
• Supporting integration of ERP, CRM, finance, production, supply chain, marketing, and operational data.
• Troubleshooting pipeline failures, performance issues, and data inconsistencies.
• Working with analysts and business teams to understand data requirements.
• Supporting dashboard, reporting, and business-intelligence initiatives.
• Designing efficient data models for analytics and reporting.
• Supporting cloud-based data infrastructure and migration initiatives.
• Implementing documentation, monitoring, validation, and data-governance practices.
• Reviewing and improving existing data-engineering processes.
• Mentoring junior team members and sharing data-engineering best practices.
• Supporting continuous improvement, automation, and scalability of the organization's data infrastructure.