Senior Engineer, Data Engineering

The Data Engineer is responsible for designing, developing, and maintaining robust, scalable, and secure data pipelines and platforms that enable efficient storage, processing, and consumption of data across the enterprise. The role ensures high-quality, timely, and governed data delivery to data scientists, analysts, and business users.
The Data Engineer plays a crucial role in building cloud-native data architectures, integrating data from diverse sources, and supporting the full lifecycle of data—from ingestion to transformation to consumption. The role ensures that the data infrastructure is reliable, compliant, and optimized to support advanced analytics and AI use cases across business functions.
Technical graduate (Engineering degree). Certifications in cloud data platforms (Azure Data Engineer, AWS Big Data, GCP Data Engineer), Big Data tools, or database technologies are preferred
Experience: 5–10 years of experience in data engineering or related roles. Proven experience in building and optimizing data pipelines, cloud data architecture, and handling large-scale datasets. Experience working with cross-functional data science, analytics, and IT teams.

Data Pipeline Development
Work Design, develop, and manage scalable ETL/ELT pipelines to extract, transform, and load data
Automate ingestion from multiple sources including APIs, files, databases, and third-party systems
Implement data ingestion from a variety of sources: ERP, CRM, APIs, file systems, IoT, etc.
Implement efficient batch and real-time data processing pipelines
Data Integration & Middleware Interaction
Integrate data from source systems via middleware platforms and API-based connectors
Leverage OData, REST, SOAP, and other data protocols to extract data from third-party platforms (e.g., SAP, Salesforce, legacy systems)
Collaborate with integration teams to establish secure and reliable connections via middleware (e.g., MuleSoft, Azure Integration Services)
Data Modelling & Architecture
Design and implement data models for operational and analytical use cases (e.g., star/snowflake schemas) . Leverage knowledge of source systems schemas for ETL design and development
Work closely with Data Architects and Analysts to ensure alignment with business needs
Optimize data storage using partitioning, compression, and indexing strategies
Cloud Data Platform Engineering
Build and manage cloud-native data solutions using services like Azure Data Factory, AWS Glue, or GCP Dataflow
Ensure optimal performance, scalability, and cost-efficiency of cloud data environments
Support migration from legacy systems to modern cloud-based architectures
Data Quality & Governance
Implement data validation, reconciliation, and monitoring frameworks
Ensure data accuracy, completeness, and integrity through automated checks
Collaborate with data governance teams to ensure metadata management and lineage tracking
Data Lineage- Document lineage and transformations to support transparency and auditability
Partner with Data Scientists, Analysts, and BI Developers to deliver data for ML models, dashboards, and reports
Provide support for data issues and troubleshoot pipeline failures
Document code, workflows, and platform configurations for maintainability
Ensure secure access control, encryption, and compliance with data privacy regulations
Work with security teams to perform vulnerability assessments on data systems
Build and Maintain the data pipelines, databases, and other systems that an organization uses to manage its data
Data Processing & Transformation: Develop processes to transform raw data into a usable format for analysis and reporting
Programming Languages: Proficiency in languages Python, SQL, Scala, Shell scripting
ETL Tools: Experience on ETL tools - Azure Data Factory, Apache NiFi, Talend, Informatica
Big Data Frameworks: Cloud Platforms: Awareness of Azure (preferred), AWS, GCP
Data Storage: Azure Data Lake, Delta Lake, Snowflake, AWS S3, BigQuery
Databases: MS SQL Server, PostgreSQL, MySQL, MongoDB
Airflow, Azure Data Factory, Prefect
CI/CD & Version Control: Git, Azure DevOps, Jenkins
Monitoring & Logging: Datadog, Prometheus, ELK Stack
Awareness of few visualization tools example- power BI etc
Data Governance Tools: Responsible for ensuring that all data is stored securely and that investments in security measures are made and regularly maintained example Collibra, Microsoft Purview, Alation (as applicable)

Coromandel International Limited is a leading Indian agrichemical company, part of the Murugappa Group and a subsidiary of EID Parry (owner of approximately 56–63%).
Founded in the early 1960s (as Coromandel Fertilisers), the company is currently headquartered in Chennai with its registered office in Hyderabad.
They are one of India’s largest private-sector producers of phosphatic fertilizers and the world’s largest manufacturer of neem-based bio-pesticides. Additionally, they lead the market in organic fertilizers and operate the country’s largest agri-retail chain, with 1000+ stores serving over 2 crore farmers.

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