Data Engineer

This role is for one of the Weekday's clients

Salary range: Rs 500000 - Rs 2000000 (ie INR 5 - 20 LPA)

Min Experience: 5+ years

Location: Mumbai, Maharashtra, India JobType: full-time

We are looking for an experienced Data Engineer to design, develop, and maintain scalable data pipelines and enterprise-grade data integration solutions. The ideal candidate will have strong expertise in Microsoft Azure data services, particularly Azure Databricks , Azure Data Factory (ADF) , and SQL Server Integration Services (SSIS) . You will work closely with data architects, analysts, and business stakeholders to build reliable data platforms that support reporting, analytics, and business intelligence initiatives.

This role requires hands-on experience in data transformation, ETL/ELT development, cloud-based data engineering, and performance optimization. The ideal candidate should be passionate about building high-quality, scalable, and efficient data solutions while ensuring data accuracy, security, and governance.

Requirements

Key Responsibilities

• Design, develop, and maintain robust ETL/ELT pipelines for ingesting, transforming, and loading data from multiple sources.
• Build and optimize scalable data processing solutions using Azure Databricks and Apache Spark.
• Develop and manage workflows using Azure Data Factory for orchestrating enterprise data movement and transformation.
• Create, maintain, and enhance SQL Server Integration Services (SSIS) packages for on-premises and hybrid data integration requirements.
• Develop optimized SQL queries, stored procedures, views, and database objects to support reporting and analytics.
• Collaborate with business teams to understand data requirements and translate them into technical solutions.
• Ensure data quality through validation, cleansing, reconciliation, and monitoring processes.
• Optimize pipeline performance, troubleshoot bottlenecks, and implement best practices for scalability and reliability.
• Integrate structured and semi-structured data from multiple enterprise systems.
• Monitor production data pipelines, resolve failures, and implement proactive monitoring mechanisms.
• Participate in code reviews, documentation, and knowledge-sharing initiatives.
• Follow data governance, security, compliance, and best practices throughout the data lifecycle.
• Support migration of legacy ETL processes to modern Azure-based data platforms where applicable.

Must-Have Skills

• 5–8 years of experience in Data Engineering or ETL Development.
• Strong hands-on expertise in Azure Databricks .
• Extensive experience with Azure Data Factory (ADF) .
• Proficiency in SQL Server Integration Services (SSIS) .
• Strong SQL programming skills with experience in query optimization and performance tuning.
• Experience developing scalable ETL/ELT pipelines.
• Good understanding of Azure Data Lake Storage and cloud-based data architectures.
• Experience with Apache Spark using PySpark or Spark SQL.
• Strong knowledge of relational databases and data warehouse concepts.
• Familiarity with data modeling, data transformation, and data integration techniques.
• Experience with source control systems such as Git.
• Strong analytical, troubleshooting, and problem-solving skills.

Good-to-Have Skills

• Experience with Azure Synapse Analytics.
• Knowledge of Azure SQL Database or SQL Server administration.
• Familiarity with Delta Lake architecture.
• Experience with CI/CD pipelines for Azure data solutions.
• Exposure to Power BI or other business intelligence tools.
• Understanding of DevOps practices for data engineering.
• Experience working with REST APIs and data ingestion from external systems.
• Knowledge of data governance, security, and compliance standards.
• Familiarity with Agile/Scrum development methodologies.

Preferred Qualifications

• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field.
• Microsoft Azure Data Engineer certification is an added advantage.
• Excellent communication and stakeholder management skills.
• Ability to work independently while collaborating effectively within cross-functional teams.
• Strong commitment to delivering high-quality, scalable, and maintainable data engineering solutions.