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 for a full-time, on-site opportunity based in Morena, Madhya Pradesh, India.
The role is suitable for experienced data professionals with backgrounds in Data Engineering, SQL, Python, ETL/ELT, Data Pipelines, Data Warehousing, Cloud Data Platforms, Data Modeling, Orchestration, and Analytics Engineering.
The Senior Data Engineer will work closely with data, software, IT, analytics, finance, production, supply chain, sales, operations, and other business teams to design and maintain reliable data platforms, build scalable pipelines, improve data quality, and enable analytics, reporting, AI, and business decision-making.
Qualifications
B.E. / B.Tech / B.Sc. / BCA / MCA / M.Sc. / M.Tech in Computer Science, Information Technology, Data Science, Software Engineering, Computer Applications, Mathematics, Statistics, or a related discipline.
Candidates with 3+ years of relevant experience in data engineering, ETL development, analytics engineering, database development, cloud data engineering, or related roles are encouraged to apply.
Candidates currently working as Senior Data Engineer, Data Engineer, Analytics Engineer, ETL Developer, Senior ETL Developer, Cloud Data Engineer, Database Developer, BI Engineer, Data Platform Engineer, or Software Engineer – Data are encouraged to apply.
Candidates from IT services, SaaS, product companies, analytics, consulting, fintech, e-commerce, telecom, FMCG, manufacturing, logistics, supply chain, or other industries are welcome.
Strong knowledge of SQL and relational database concepts.
Strong programming skills in Python.
Experience with databases such as PostgreSQL, MySQL, SQL Server, Oracle, or similar platforms.
Strong understanding of ETL/ELT, data pipelines, data modeling, data warehousing, and data-integration concepts.
Experience with tools such as Apache Airflow, dbt, SSIS, Informatica, Talend, Azure Data Factory, AWS Glue, or similar orchestration/ETL technologies will be beneficial.
Exposure to Snowflake, Databricks, BigQuery, Amazon Redshift, Azure Synapse, or similar data platforms will be highly beneficial.
Experience with AWS, Microsoft Azure, or Google Cloud Platform will be an advantage.
Familiarity with PySpark, Apache Spark, Kafka, Hadoop, or distributed data-processing technologies will be beneficial.
Strong understanding of data warehousing, dimensional modeling, fact/dimension tables, star schemas, and analytical datasets.
Experience integrating data using REST APIs, JSON, CSV, files, databases, and enterprise applications.
Familiarity with Power BI, Tableau, Looker, or other BI platforms will be beneficial.
Experience with Git, GitHub, version control, testing, debugging, and collaborative engineering workflows.
Exposure to Docker, Kubernetes, CI/CD, Linux, Jenkins, GitHub Actions, or DevOps practices will be an advantage.
Understanding of data quality, governance, lineage, security, access control, and documentation.
Experience optimizing SQL queries, pipelines, warehouse workloads, and data-processing performance.
Experience with monitoring, logging, alerting, and troubleshooting production data pipelines.
Experience mentoring junior engineers, conducting code reviews, or taking ownership of data-engineering projects will be beneficial.
Strong analytical, technical, troubleshooting, and problem-solving abilities.
Good communication, documentation, coordination, and stakeholder-management skills.
Ability to work effectively with data, software, analytics, and business teams.
Willingness to work in an on-site environment.
Strong ownership mindset and ability to independently manage complex data pipelines and engineering initiatives.

Work arrangement
No