The Team

Deloitte’s Technology & Transformation practice helps organizations modernize their data platforms, strengthen data management capabilities, and streamline enterprise reporting through technology-driven transformation. Our teams work across data engineering, cloud data platforms, enterprise reporting, data warehousing, and analytics to deliver scalable, reliable, and high-quality data solutions. The practice combines cloud technologies, data integration, process optimization, and governance to improve data accuracy, operational efficiency, and informed business decision-making.

Your Work Profile

As a Data Engineering Specialist, you will work closely with data engineering teams, business stakeholders, and cross-functional teams to design, develop, maintain, and optimize scalable data pipelines and enterprise data solutions. You will be responsible for data integration, ETL development, transformation, data quality validation, troubleshooting, and performance optimization across cloud-based and on-premises data platforms.

You will leverage Azure Databricks, Azure Data Factory, PySpark, Spark SQL, and Delta Lake to build robust data processing solutions and support lakehouse architecture. You will also contribute to data warehousing, database development, API integrations, deployment automation, defect analysis, release management, and continuous improvement initiatives while ensuring adherence to established engineering standards and data governance practices.

Key Skills Required

• Strong experience in data engineering, ETL development, data integration, and enterprise data warehousing.

• Hands-on expertise in Azure Databricks, Azure Data Factory, PySpark, Spark SQL, and Delta Lake.

• Strong SQL and PL/SQL skills, including complex queries, stored procedures, performance tuning, and data transformation.

• Experience designing and developing scalable data pipelines and implementing lakehouse architecture.

• Experience with Informatica PowerCenter and enterprise ETL workflows.

• Knowledge of relational databases, including Oracle, PostgreSQL, and SQL Server.

• Experience working with Azure Data Lake Storage Gen2 (ADLS Gen2) and cloud-based data processing environments.

• Familiarity with REST APIs and ServiceNow API integrations for data exchange and automation.

• Experience using Git, GitHub, CI/CD pipelines, and GitHub Actions for source control and deployment automation.

• Working knowledge of Unix/Linux environments and shell scripting for automation and operational support.

• Experience in defect analysis, root-cause investigation, regression testing, release management, and production issue resolution.

• Understanding of data governance, data quality, documentation, and engineering best practices.

• Ability to collaborate with stakeholders and cross-functional teams to deliver reliable, maintainable, and scalable data solutions.

Experience

• Relevant experience in data engineering, ETL development, cloud data platforms, and enterprise data integration.

• Hands-on experience with Azure Databricks, Azure Data Factory, PySpark, Spark SQL, and Delta Lake.

• Experience with relational databases, data warehousing, and enterprise ETL tools.

• Exposure to CI/CD automation, release management, testing, and production support is preferred.

Languages

• Strong verbal and written communication skills.

• Ability to document technical specifications, data workflows, implementation procedures, and troubleshooting steps.

• Proficiency in business presentation and documentation tools, including PowerPoint, Excel, and Word.

• Ability to collaborate effectively with technical teams, business stakeholders, and cross-functional teams.

Technologies

Primary Skills: Azure Databricks, Azure Data Factory, PySpark, Spark SQL, Delta Lake, ETL, SQL, PL/SQL, Informatica PowerCenter, Data Warehousing, Lakehouse Architecture, Oracle, PostgreSQL.

Cloud & Data Integration: Azure Data Lake Storage Gen2 (ADLS Gen2), REST APIs, ServiceNow API.

Version Control & DevOps: Git, GitHub, CI/CD, GitHub Actions.

Operating Systems & Scripting: Unix, Linux, Shell Scripting.

Databases & Legacy Technologies: SQL Server, PowerBuilder.

Data Quality & Governance: Data Governance, Defect Analysis, Root-Cause Analysis, Data Validation, Regression Testing, Release Management.

Education

• Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related discipline.

• Relevant certifications in Azure, Databricks, or data engineering are preferred.

Location and Way of Working

• Base Location: Bengaluru

• Work Mode: Hybrid