Top Data Engineer Jobs in Bangalore

As a Data Engineer I, you will develop and optimize data pipelines for payment processing, advocate for data best practices, and collaborate with teams to understand data needs.

The Data Engineer optimizes the elluminate® platform, configures ETL processes, develops code, and ensures compliance with quality standards for clinical trials.

The Data Engineer II provides technical consulting and support for the elluminate platform, including SQL development, project management, and client training.

The Data Engineer will optimize core data infrastructure for renewal and churn analytics, develop data pipelines, and collaborate with cross-functional teams to support business growth and retention strategies.

As a Data Engineer, you will build data pipelines, design data models, and deploy Data Ops, collaborating with multiple teams to enhance product offerings.

The role involves creating and optimizing data pipelines, developing semantic layers, and building data applications to enhance data accessibility and analytics. Requires extensive experience in Java and related technologies.

The Data Engineer will be responsible for delivering high-quality, large-scale data applications, managing data pipelines, and productizing AI/ML models.

The Sr Data Engineer II is responsible for technical consulting, project management, SQL development, client support, and maintaining compliance with industry standards.

The Sr Data Engineer I designs and develops SQL code, manages projects, provides technical support, and oversees clinical data services.

The Data Engineer III will perform data analysis, collaborate across teams, support project delivery, and ensure data quality in ETL processes using cloud platforms like AWS and Azure.

As an SDE 2 in Analytics, you'll design and maintain data models, build ELT pipelines, and ensure data quality while supporting product teams with analytics solutions.

The Data Engineer will design and develop data solutions, data pipelines, and data management processes, while collaborating globally to enhance data strategy and implementation at JLL.

Join as a Data Engineer to manage end-to-end data workflows, focusing on SQL, Python or Scala, and Azure for scalable data solutions.

The Data Engineer transforms data into structured formats for analysis, develops ETL packages, creates data models, and collaborates with stakeholders for data requirements.

The Data Engineer transforms data into structured formats for analysis, develops data models, creates ETL packages, and collaborates across teams to support business analytics needs.

As a Data Engineer I, you'll design and implement ETL data pipelines, optimize data solutions in AWS and Snowflake, and ensure data quality and efficiency.

As a Data Engineer, you will work with technologies like Python and PySpark to manage and process data efficiently using tools such as Hive and Hadoop HDFS.

The Data Engineer I will develop, optimize, and manage data infrastructure, focusing on database design, ETL processes, data modeling, and integration to ensure data reliability for analysis and reporting.

As a Data Engineer II, you will develop, optimize, and manage data lakes and warehouses. Responsibilities include designing data pipelines, ensuring data quality, and leveraging cloud platforms for data management.

The Senior Manager, Data Engineer will lead data architecture and engineering projects for Visa clients, focusing on scalable data solutions in the BFSI sector, collaborating across teams, and providing strategic consulting and technical expertise in big data technologies.

As a Data Engineer, you will architect and develop enterprise-grade data pipelines using Azure technologies, focusing on modernizing BI data landscapes and ensuring data quality and governance through CI/CD practices.

As a Data Engineer, you will implement data pipelines and analytics to support decision-making using AWS technologies and Machine Learning.

As an Analytics Engineer III, you will develop Power BI reports and dashboards, manage data models, optimize performance, and collaborate with stakeholders to align data solutions with business needs.

Develop and deploy data solutions using industry standards while collaborating with data scientists. Follow agile methods to deliver features and solutions.

Develop and manage data lakes and warehouses, optimize data pipelines, ensure data quality, and provide troubleshooting support.