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.

Summary

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See also