Data Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Engineer based in India.

This role is an opportunity to contribute to the design and delivery of scalable, cloud-based data solutions within a modern data engineering environment. You will work on building and optimizing ETL/ELT pipelines that enable reliable data flow across multiple systems and business domains. The position involves hands-on development using Python, PySpark, and SQL, with a strong focus on Azure cloud technologies. You will collaborate closely with data analysts, engineers, and business stakeholders to transform raw data into meaningful, high-quality insights. This is a highly collaborative and technically engaging role where your work directly supports data-driven decision-making. You will also be involved in improving data reliability, performance, and governance across evolving data platforms.

• Design, develop, and maintain scalable ETL/ELT pipelines to support efficient data integration and processing across multiple sources.
• Build and optimize data processing workflows using Python and PySpark for batch and large-scale data workloads.
• Ingest and transform data from APIs, databases, files, and streaming systems into structured formats for analytics and reporting.
• Write and optimize SQL queries to support data transformation, reporting, and performance-efficient data retrieval.
• Monitor, troubleshoot, and improve existing data pipelines to ensure reliability, scalability, and performance.
• Support data quality, validation, and governance initiatives, including metadata management and data lineage tracking.
• Work with Azure Data Factory, Azure Data Lake, and Azure Databricks to develop and maintain cloud-native data solutions.
• Collaborate in Agile environments, participating in sprint planning, code reviews, and cross-functional delivery cycles.

Requirements

• 2–4 years of experience in data engineering or related roles, with hands-on exposure to building data pipelines and cloud-based solutions.
• Strong proficiency in Python, SQL, and PySpark for data processing and transformation.
• Experience working with Microsoft Azure services such as Azure Data Factory, Azure Data Lake, and Azure Databricks.
• Solid understanding of ETL/ELT architecture, data integration patterns, and data warehousing concepts.
• Familiarity with version control tools such as Git and basic understanding of CI/CD practices.
• Exposure to streaming technologies such as Apache Kafka is an added advantage.
• Strong analytical thinking, problem-solving skills, and ability to work both independently and in Agile team environments.
• Good communication skills with the ability to collaborate effectively with technical and non-technical stakeholders.

Benefits

• Remote-friendly work environment with flexible engagement options
• Opportunity to work with modern cloud data technologies (Azure ecosystem)
• Career growth in a fast-evolving data engineering and analytics environment
• Exposure to large-scale data pipelines and enterprise-level data systems
• Collaborative, Agile-driven engineering culture
• Learning opportunities in data engineering, cloud architecture, and data governance practices
• Inclusive work environment focused on continuous improvement and technical excellence